CYPRUS UNIVERSITY OF TECHNOLOGY

BSc in Computer Science and Engineering

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STRUCTURE & CONTENT

Computer Science and Computer Engineering are among the most modern and rapidly evolving scientific fields. They integrate principles of computer science and computer engineering to enable the design and development of innovative computing systems, advanced algorithms, secure networks and databases, and to leverage technologies such as artificial intelligence, robotics, cybersecurity, and data analytics. Graduates of the Computer Science and Engineering programme design, develop, and evaluate integrated hardware and software systems, contributing to the advancement of cutting-edge technological applications across diverse domains.

The Computer Science and Engineering programme is four years in duration and includes foundational compulsory courses, advanced specialisation courses, academic English courses, and elective courses offered by other University Departments. It combines a strong theoretical foundation with practical skills and excellent training in the design, analysis, and evaluation of computing systems. The programme focuses on advanced mathematics, physics, programming, databases, signals and systems, bioinformatics, networks and communications, artificial intelligence, computer architecture, and cybersecurity. In the fourth year, students undertake an individual undergraduate thesis under the supervision of academic staff of the Department.

Graduates of the programme are eligible for registration as members of the Cyprus Scientific and Technical Chamber (ETEK), enjoying all professional rights granted to Engineers.

SEMESTER MODULES

TABLE 1: STRUCTURE OF THE PROGRAMME OF STUDY

PROGRAMME REQUIREMENTS	NUMBER	ECTS
Compulsory courses	36	192
DEPARTMENTAL ELECTIVES ENGINEERING ELECTIVES FREE ELECTIVES	2	10
	2	10
	3	15
THESIS I & II	2	15
Total	45	242

TABLE 2: COURSE DISTRIBUTION PER SEMESTER

Α/Α	Course type	Course title	Course code	Periods per week	Period duration	Number of weeks/ Semester	Total periods/ Semester	Number of ECTS
Α’ Semester
1	Compulsory	Advanced Mathematics I	EEI 101	2T	2	13	26	5
2	Compulsory	Physics I	EEI 102	2T	2	13	26	5
3	Compulsory	Electrical and Electronic Circuits	CSE 121	2T+1L	2	13	39	6
4	Compulsory	Introduction to Computing and Programming (C)	CSE 132	2T+1L	2	13	39	6
5	Compulsory	English for Computer Science and Engineering I	LCE 115	2T	1.5	13	19.5	4
6	Compulsory	Fundamentals of Electrical Engineering, Computer Science and Engineering	CSE 120	2T	2	13	26	6
Β’ Semester
1	Compulsory	Advanced Mathematics II	EEI 103	2T	2	13	26	5
2	Compulsory	Linear Algebra	EEI 104	2T	2	13	26	5
3	Compulsory	Digital Logic	CSE 122	2T+1L	2	13	39	5
4	Compulsory	Free Elective						5
5	Compulsory	Object Oriented Methodology and Programming with C++	CSE 133	2T+1L	2	13	39	6
6	Compulsory	English for Computer Science and Engineering II	LCE 116	2T	1.5	13	19.5	4
C’ Semester
1	Compulsory	Discrete Mathematics	CSE 221	2T	2	13	26	5
2	Compulsory	Analysis and Design of Information Systems	CSE 223	2T+1L	2	13	39	5
3	Compulsory	Probabilities and Random Processes	EEI 234	2T	2	13	26	5
4	Compulsory	Principles of Programming Languages and Compilers	CSE 222	2T+1L	2	13	39	6
5	Compulsory	Data Structures and Algorithms	CSE 224	2T+1L	2	13	39	5
6	Compulsory	Operating Systems	CSE 225	2T+1L	2	13	39	5
D’ Semester
1	Compulsory	Bioinformatics	CSE 229	2T	2	13	26	6
2	Compulsory	Systems Programming	CSE 228	2T+1L	2	13	39	5
3	Compulsory	Algorithms and Complexity	CSE 226	2T+1L	2	13	39	5
4	Compulsory	Signals and Systems	EEΙ 231	2T+1L	2	13	39	6
5	Compulsory	Computer Organization	EEI 233	2T+1L	2	13	39	6
6	Compulsory	Advanced Object-Oriented Methodology and Programming (UML, Java)	CSE 227	2T+1L	2	13	39	6
E’ Semester
1	Compulsory	Computer Networks and Communications	CSE 321	2T	2	13	26	5
2	Compulsory	Computer Networks and Communications Laboratory	CSE 322	2L	2	13	26	4
3	Compulsory	Computer Architecture	CSE 323	2T+1L	2	13	39	6
4	Compulsory	Software Engineering	CSE 324	2T+1L	2	13	39	5
5	Compulsory	Databases	CSE 325	2T+1L	2	13	39	5
F’ Semester
1	Compulsory	Applied HCI with Generative AI	CSE 326	2T+1L	2	13	39	6
2	Compulsory	Parallel Processing and Distributed Systems	CSE 327	2T	2	13	26	6
3	Compulsory	Software Engineering Project and Professional Practice	CSE 328	2T+1L	2	13	39	7
4	Compulsory	Statistical Pattern Recognition and Machine Learning	CSE 329	2T+1L	2	13	39	6
5	Compulsory	Departmental Elective						5
G’ Semester
1	Compulsory	Thesis Ι	CSE 421	-	2	13	-	5
2	Compulsory	Introduction to Cryptography and Computer Security	CSE 423	2T	2	13	26	5
3	Compulsory	Deep Learning I	CSE 471	2T	2	13	26	5
4	Compulsory	Departmental Elective						5
5	Compulsory	Elective from the Faculty of Engineering						5
6	Compulsory	Free Elective						5
H’ Semester
1	Compulsory	Thesis ΙI	CSE 422	-	2	13	-	10
2	Compulsory	Introduction to Data Science	CSE 473	2T	2	13	26	5
3	Compulsory	Deep Learning II	CSE 472	2T	2	13	26	5
4	Compulsory	Elective from the Faculty of Engineering						5
5	Compulsory	Free Elective						5

Elective Modules
Departmental Electives (Fall Semester)
1	Mobile Computing and Applications	CSE 469	2T	2	13	26	5
2	Electronics I	EEN 214	2T	2	13	26	5
3	Electrical Laboratory II	EEN 215	1L	4	13	13	5
Departmental Electives (Spring Semester)
1	Machine Learning Systems at Scale	CSE 461	2T	2	13	26	5
2	Integrated Circuits Design and Testing	CSE 470	2T	2	13	26	5
3	Advanced Topics in Software Engineering	CSE 463	1T	3	13	13	5
4	Introduction to Finance & Management Principles for IT Companies	CSE 474	2T	2	13	26	5
Other Departmental Electives
1	Photonics	EEN 413	2T	2	13	26	5
2	Power System Analysis and Protection	EEN 442	2T	2	13	26	5
3	Power Electronics	EEN 443	2T	2	13	26	5
4	Digital Image Processing	EEN 444	2T	2	13	26	5
5	Renewable Energy Sources	EEN 445	2T	2	13	26	5

ΑΛΥΣΙΔΕΣ ΜΑΘΗΜΑΤΩΝ

ΑΑ	Course	Requirements
1	CSE 226 - Algorithms And Complexity	CSE 224 – Data Structures and Algorithms
2	EEI 233 – Computer Organization	CSE 132 – Introduction to Computing and Programming (C)
3	CSE 323 – Computer Architecture	EEI 233 – Computer Organization
4	CSE 328 - Software Engineering Project And Professional Practice	CSE 324 – Software Engineering
5	CSE 422 – Thesis ΙΙ	CSE 421 – Thesis Ι

ENTRANCE EXAMS (NATIONAL EXAMS - COURSES)

TABLE 19

COMPULSORY (3)

ELECTIVE (1)

Modern Greek
Mathematics
Physics

Chemistry,
Biology,
Computer Science,
Freehand Sketching,
Architecture-Technical Sketching or Architecture Sketching Techn. (Th.Dir.),
Design and Technology or Technology Techn.Schetching. (Th.Dir.)

CSE 120 Fundamentals of Electrical Engineering, Computer Science and Engineering

Course title	Foundations of Electrical and Computer Engineering and Informatics
Course code	CSE-120
Course type	Compulsory
Level	Undergraduate
Year / Semester	1st year/1st semester
Teacher’s name	Christos Loizou
ECTS	6	Lectures / week	2x2h		Laboratories / week		-
Course purpose and objectives	Introduction, presentation and discussion of the fundamental laws and axioms of electrical engineering and computer engineering and informatics. The course aims to introduce students to the basic theories on which the two above fields are based and to give them the opportunity to present and develop their own ideas for solving various problems faced by this scientific subject. During the course, an introduction is made to subjects such as fundamental knowledge of engineering, fundamental quantities and units of measurement, history and development of electrical engineering, introduction to signal processing, analog and digital quantities, sources of electrical energy and simple circuit connections, electricity production in Cyprus, electrical installations, grounding of electrical devices and circuits, electric shock, introduction to electrical circuits and simple connections, statistics, statistical signal analysis. Presentations are also made by all students on selected subjects of their own interests.
Learning outcomes	Understand the basic principles of electrical engineering, biotechnology, computer engineering, and informatics. Explain and apply the fundamental axioms of electrical engineering in creating, managing, transferring, and utilizing energy. Describe the history and development of electrical engineering and apply knowledge of circuits, energy production, installations, and grounding. Apply statistics and statistical signal analysis in electrical and computer engineering contexts.
Prerequisites	None			Required		None
Course content	Presentations can be made on various topics (some are given below): Electric and autonomous cars Renewable energy sources Air conditioning and coronavirus, or other respiratory diseases Solar energy, renewable energy sources The big questions for the G5 The evolution of cars and the consequences on the environment Arduino, uses and applications Facial processing and analysis for emotion detection Virtual reality Information management through computer science and the internet Quantum computer Medicine and technology Telemedicine, data transfer, storage, compression, processing, analysis Signal and image processing for medical assessment The operating room of the future.
Teaching methodology	Lectures and student presentations.
Bibliography	Class notes and research articles.
Assessment	Student presentations (30%) Peer assessment in class, quizzes (10%) Attendance, class and discussion participation, quizzes (10%) 4. Written final exam (50%)
Language	Greek

CSE 121 Electrical and Electronic Circuits

Course Title	Electrical and Electronic Circuits
Course Code	CEI 121
Course Type	Compulsory
Level	Undergraduate
Year / Semester	1st / 1st
Teacher’s Name	Marios Lestas
ECTS	6	Lectures / week		2 (2 hours)	Laboratories / week		1 (2 hours)
Course Purpose and Objectives	Develop the theory of Electrical and Electronic Circuit Analysis (with the help of characteristic examples) in a systematic and simple way, so that the general problems of Electrical and Electronics can be understood and approached in an easy and correct way.
Learning Outcomes	Understand the basic areas of Electrical and Electronics relevant to Computer Engineering. Apply fundamental laws such as Ohm's Law, Kirchhoff’s Laws, and circuit theorems in analyzing electrical circuits. Analyze the construction and operation of diodes, transistors, MOSFETs, and logic circuits. Analyze circuits using theorems and analysis methods Understand and apply operational amplifiers and their applications in analog and digital systems. Demonstrate knowledge of DAC, ADC, and sampling systems in digital/analog conversion.
Prerequisites	None		Required			None
Course Content	Ohm's Law, Voltage Divider, Current Divider Kirchhoff’s Laws (Current and Voltage Law) Theories and Circuit Analysis Electric Power and Maximum Power Transfer Construction and Operation of Diode, Bipolar Contact Transistor, Field Effect Transistors (JFET, MOSFET and new types) Polarization, Polarity stability, Switching characteristics, propagation delays Enhancement. Gate circuits and topologies (BJT, NMOS, CMOS and newer types): ideal and real gate transport characteristic, input-output levels Basic families of logic circuits Operational Amplifiers Ideal Operational Amplifier. Simple Applications Comparator, filters Converters from Digital to Analog (DAC) and from Analog to Digital (ADC), Sampling System (SAH).
Teaching Methodology	Lectures, tutorials, research exercises.
Bibliography	Ανάλυση Ηλεκτρικών Κυκλωμάτων , Τόμος Β’, Νικόλαου Η. Παπαμάρκου, Εκδόσεις Β. Γκιούρδα, 2007, ISBN -13: 978 – 960 – 387 – 579 – 6
Assessment	Weekly homework assignments and a project and midterm exam 30 % Laboratory Exercises 20% Final Exam 50%
Language	Greek

CSE 122 Digital Logic

Course code

CSE 122

Course type

Mandatory

Level

Undergraduate

Year / Semester

1st year/22n semester

Teacher’s name

Kyriakos Deliparaschos

ECTS

Lectures / week

2 (4h)

Laboratories / week

1 (2h)

Course purpose and objectives

The goal is to provide students with fundamental knowledge in digital systems, focusing on their analysis, specification, operation, design, and implementation. It also aims to provide knowledge in digital design using design automation tools and hardware description languages, along with complementary knowledge of the basic concepts of digital design through a combination of lectures, exercises, and labs.

Learning outcomes

Understand number systems, Boolean algebra, and minimization of logical functions.
Analyze and design combinational and sequential digital circuits including logic gates, adders, multiplexers, and flip-flops.
Apply Verilog HDL for modeling, simulation, and implementation of digital circuits.
Gain experience with programmable logic devices such as CPLDs and FPGAs.
Understand the complete digital design cycle including simulation, verification, and VLSI implementation.

Prerequisites

Required

Course content

The course is based on the fundamental elements of logic circuit design and has the following main objectives:

Introduction to the design of combinational and sequential digital circuits.
Use of the hardware description language Verilog for modeling, simulation, and implementation of these circuits.
Implementation of digital systems on programmable logic devices (CPLDs and FPGAs).
Gaining experience in the full cycle of the design, simulation, and implementation process of digital systems using modern computer tools through laboratory exercises.

The following topics/chapters are covered:

Number Systems (Digital systems, Binary numbers, Conversion of numbers to other base formats, Complements, Signed binary numbers, Registers, and binary logic).
Boolean Algebra(Definitions, basic theorems and properties, Boolean functions, Normal and standard forms, Logic gates, Digital integrated circuits, digital logic families (TTL, ECL, MOS, and complementary MOS-CMOS), levels of integration).
Minimization of Logical Functions (Karnaugh map method, Rules for the maps, Karnaugh maps for four and five variables, Simplification of sum-of-products, Implementation with NAND and NOR gates, Don't care conditions, XOR function, and error detection and correction circuits).
Combinational Logic (Analysis and design process of combinational circuits, Binary adders and subtractors, Binary multipliers, Size comparators, Encoders/Decoders, Multiplexers).
Modern Sequential Logic (Latches and Flip-Flops, Analysis of sequential circuits with clock, Minimization and state encoding, Size comparators, Encoders and Decoders, Design process with Flip-Flops).
Registers, Counters, Memories, and Programmable Logic (Registers/Shift registers, Ripple counters, Synchronous counters, Other types of counters, Read-only memory (ROM), Random access memory (RAM), Memory decoding, Error detection and correction, PLA, PAL, Sequential PLDs (SPLD), CPLD, FPGA).
Asynchronous Sequential Logic (Analysis process, Circuits with latches, Design process, Minimization of state and flow tables, State encoding to avoid race conditions, Hazards).
Hardware Description Languages (HDL) and Design Automation Tools (EDA) (Basic concepts, Verilog for combinational circuits, Verilog for sequential circuits, Register transfer level (RTL) design, Finite state machines (FSM), Top-down analysis and design, Circuit simulation and verification, Logic synthesis, Placement and routing, Technology mapping and element libraries, Fully customized integrated circuits (full-custom), Semi-custom standard cells, and gate-array integrated circuits).

Teaching methodology

Presentations, solving exercises, group discussion, examples and use cases, and laboratory exercises.

Bibliography

M. Morris Mano, “Ψηφιακή Σχεδίαση”, Εκδόσεις Παπασωτηρίου. (ISBN: 9789604911134)
Verilog-HDL, Samir Palnitkar, Prentice Hall
Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation (Systems on Silicon)
Randy H. Katz, “Contemporary Logic Design”, 2nd Edition, Prentice Hall (ISBN: 0201308576)
Lecture notes
Research articles

Assessment

Laboratory Exercises	20%
Midterm Exam (written)	25%
Final Exam (written)	55%

Language

Greek

CSE 132 Introduction to Computing and Programming (C)

Course title	CSE 132
Course code	Introduction to Computation and Programming in C
Course type	Compulsory
Level	Undergraduate
Year / Semester	1st Year, 1st Semester
Teacher’s name	Simos Gerasimou
ECTS	6	Lectures / week	4		Laboratories / week		2
Course purpose and objectives	The course of the Introduction to Computations and Programming aims to further enrich students' knowledge in the organization of computer hardware (microprocessors and memory structures) and computation (Turing machines, finite state machines, normal languages, frameless grammars). Introduction to modular programming. Presentation of the process of development of functional and procedural software and introduction of the basic principles of programming and program design using the C language. A general introduction of the C language focusing on data types, control structures, iterative structures, functions, tables, alphanumeric sequences, pointers, and recursion. The course includes several programming exercises and a semester paper.
Learning outcomes	Describe the historical evolution of computers. Explain the organization of a computer. Distinguish between programming methodologies and generations of programming languages. Design and implement programs in C using data types, control structures, functions, arrays, and pointers.
Prerequisites	-			Required		-
Course content	Topics to be considered include: Algorithms, C commands such as if, if/else, while, do/while, for, etc. as well as functions, various libraries, header files, random numbers, storage classes and scope rules, recall, tables and fonts, multidimensional tables, indexes, income/expense formatting and file editing.
Teaching methodology	Presentations, group discussion, and workshop.
Bibliography	Notes from a lecturer. C from theory to application (G. S. Tseliki and N. D. Tselikas. (ISBN: 9780429156922) C how to program by Deitel and Deitel (ISBN: 9780133976892)
Assessment	Midterm (35%), assignments (10%), participation in workshops (5%), final examination (50%).
Language	Greek

CSE 133 Object Oriented Methodology and Programming with C++

Course title	Object Oriented Methodology and Programming (C++)
Course code	CSE 133
Course type	Mandatory
Level	Undergraduate
Year / Semester	First, Spring
Teacher’s name	Simos Gerasimou
ECTS	6	Lectures / week	4		Laboratories / week		2
Course purpose and objectives	This course aims to further enrich students ' knowledge in object-oriented programming with C++. The course teaches C++ starting from simple concepts such as classes, objects, inheritance, I/O flow classes, reference to C++ types, markers, struct, etc. The concepts of complexity and overload of operators are also taught. Modules include: Classes, derivative classes, input/output classes, substructs, pointers, inheritance, function and operator overload, complexity.
Learning outcomes	After the successful completion of the course, the learner should: Differentiate between object-oriented and structured programming. Use classes, objects, member functions, and member data in C++ programs. Explain and apply access identifiers (public, private, protected), constructors, and destructors. Organize code by separating interface, implementation, and client files for reusable software projects Apply OOP concepts such as encapsulation, inheritance, polymorphism, and virtual methods Solve problems using control structures, pointers, functions, arrays, vectors, and files Use standard C++ libraries effectively in software development.
Prerequisites	-			Required		-
Course content	The topics to be examined include: Classes and all general concepts of object-oriented programming such as encapsulation, inheritance, polymorphism, and virtual methods. All C++ commands, such as control commands, functions, various libraries, random numbers, storage classes and scope rules, recursion, tables and fonts, multi-dimensional tables, pointers, format input/output and files.
Teaching methodology	Presentations, group chats, and laboratories.
Bibliography	Notes from instructor. C how to program by Deitel and Deitel (ISBN: 9780133976892)
Assessment	Mid-Term (35 %), Homework (10 %), Final (55 %).
Language	Greek

CSE 221 Discrete Mathematics

Course Title	Discrete Mathematics
Course Code	CSE 221
Course Type	Mandatory
Level	BSc Degree
Year / Semester	2nd / 1st
Teacher’s Name	Paul Christodoulides
ECTS	5	Lectures / week		2	Lectures / week		-
Course Purpose and Objectives	To give a broad insight into basic notions of discrete mathematics in relation to mathematical logic, boole algebra and graph theory, which can become tools for better understanding and solving related computer science applications.
Learning Outcomes	Acquire foundational knowledge of mathematical logic, propositions, quantifiers, and proofs. Understand and apply set theory, relations, functions, and equivalence relations. Apply Boolean algebra, combinatorics, permutations, and combinations to problem-solving. Analyze algorithms for complexity and efficiency, and understand numeral systems. Apply graph theory concepts including paths, trees, and spanning trees in computer science problems.
Prerequisites	-		Required			-
Course Content	Mathematical logic, propositions, quantifiers, arguments, Introduction to proofs, Sequences, mathematical induction principles, recursion theory, Sets theory, Relations, functions, equivalence relations, Boole algebra and applications, Combinations and permutations, Algorithms complexity and efficiency, Numeral systems, Graphs, paths, algorithms, trees, spannning tree.
Teaching Methodology	Presentations, Solution of exercises, Group discussion
Bibliography	K. Rosen, Discrete Mathematics with Applications, McGraw-Hill Higher Education (ISBN: 0073383090) S. Epp, Discrete Mathematics with Applications, Thomson (ISBN: 0495391328) S. Lipschutz, M. Lipson, Schaum’s Outlines: Discrete Mathematics, McGraw-Hill (ISBN: 0071615865) Π. Χριστοδουλίδης, Σημειώσεις Διαλέξεων στα Διακριτά Μαθηματικά (in Greek)
Assessment	Mid-semester exam (30%), Assignments (20%), Final exam (50%)
Language	Greek

CSE 222 Principles of Programming Languages and Compilers

Course Title

Principles of Programming Languages and Compilers

Course Code

CSE 222

Course Type

Compulsory

Level

Undergraduate

Year / Semester

2nd / 3rd

Teacher’s Name

Special Scientist

ECTS

Lectures / week

2 (2 hours)

Laboratories / week

1 (2 hours)

Course Purpose and Objectives

This course is an introduction to the various phases of compiling a programming language. Reference is made to the main concepts of programming languages to illustrate the steps followed by a compiler to convert a source program into machine language. The course therefore deals with the verbal, syntactic, semantic analysis of an original programming language and its conversion into intermediate code and final language. The flex and bison tools are used for the practical application and development of small speech analysts and editorial analysts respectively.

Learning Outcomes

Upon completion of the course, successful students shall be able to:

Explain how analysis, regular expressions and languages work with the application of word analysts.
Explain syntax analysis, top-down and bottom-up analysis, the use of syntactic analysis.
Explore how semantic analysis and intermediate code work.
Explain issues related to memory organization and execution environment (runtime environment), register allocation and final code generation and optimization.
Implement a simple compiler for the MIPS processor. This will be implemented through the C language and the Flex and Bison tools.

Prerequisites

Basic language proficiency programming is a prerequisite

Required

Course Content

Week 1: Introduction, Programming Languages, Compilers, Necessity and historical overview, Compiler types and related tools, Basic compiler structure.
Week 2: Standard Languages, Genitive and Cognitive Models, Grammar, Chomsky Hierarchy, Identifiers, Normal Languages, Regular Expressions, Finite Auto.
Week 3: Verbal Analysis, Transition Charts, Speech Analyzer Manufacturing.
Week 4: Contextual Languages, Conflicting Languages, Contextual Language Representation Ways.
Week 5: Editorial analysis, bottom-up analyst.
Week 6: Error recovery, use of compilers to create editorial scripts.
Week 7: Mid-term examination
Week 8: Structural Analysis, Ancillary Concepts, FIRST and FOLLOW Calculation, Upward and Downward Structural Analyzers, Upward Structural Analysis.
Week 9: Semantic Analysis, Static Semantics, Dynamic Semantics, Semantic Check, Formula Check, Formula Extraction, Categorical Grammar, Symbol Table.
Week 10: Intermediate Code Generation, Intermediate Language Forms, Quadratic Language, Operating, Operators.
Week 11: Intermediate Code Generation, A Simple Four-Generation Plan, Arithmetic Expressions, Logical Expressions, Simple Commands, Advanced Command, If Command, While Command, Call Subroutines.
Week 12: Final Code Generation.
Week 13: Code Optimization, Code Optimization Techniques.
Week 14: Preparation for Final Exam

Teaching Methodology

The course is taught through:

• Interactive face-to-face lectures

• Group activities / discussions

• In-class activities

• Multimedia activities

• Laboratory

Bibliography

Basic manuals

Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman, Compilers: Principles, Techniques, and Tools, 2nd Edition, Pearson, 2013. (ISBN: 0321486811)
“Μεταγλωττιστές”, Νικόλαος Παπασπύρου και Εμμανουήλ Σκορδαλάκης, Εκδόσεις Συμμετρία, 2002 (ISBN 978-960-266-135-2).

Recommended

“Engineering a Compiler”, Keith D. Cooper and Linda Torczon, Elservier MK Morgan Kaufmann. (ISBN: 9780128154120)
Terence Halsey, Compiler Design: Principles, Techniques and Tools, Larsen and Keller Education, 2018. (ISBN: 1635496772)
“Flex & Bison”, John Levine, O’ Reilly, 2009.

Assessment

	Percentage
Homeworks	10%
Lab Assignments / Project	25%
Midterm Exam	25%
Final Exam	40%

Language

Greek

CSE 223 Analysis and Design of Information Systems

Course Title	Analysis and Design of Information Systems
Course Code	CSE 223
Course Type	Compulsory
Level	Undergraduate
Year / Semester	Fall
Lecturer	Eliza Angelidou Loizou
ECTS	5	Lecures / week		4	Labs / week	2
Course Purpose and objectives	Learning how to use Systems Analysis and Design tools for developing Information Systems. Learning the Phases and Activities of Systems Analysis and Design and how to follow them in developing systems. Theoretical and practical knowledge in the areas of System Management, the role, duties and responsibilities of the Systems Analyst, how the teams should be formed and organized. Understanding the Systems Development Life Cycle and its stages. Developing a feasibility study for the system to be developed.
Learning Outcomes	Analyze and design an information system by following the phases and activities of systems analysis and design Apply project management, feasibility studies, and team organization in information system development Use information gathering techniques, data modeling, and process modeling for system analysis Develop and present a new system proposal and design, including human-computer interface considerations Apply CASE tools and laboratories to support systems analysis and design practice
Prerequisites			Required
Course Content	Introduction to various Systems Analysis and Design methodologies and terminology to be used throughout the course. The Phases and Activities used for analysing and developing an Information System. Teaching and practical training in the fields of project management, communication, information gathering and feasibility study. SECTIONS – Chapters: Who is involved in Systems Analysis and Design Phases and Activities of Systems Analysis and Design Developing Information Systems Project Management / Feasibility Study System Analysis Gathering and analyzing the new system requirements Data Modelling Process Modelling The New System proposal System Design Case Tools Designing the Human-Computer Interface
Teaching Methods	Class Presentations Presentation of Case Studies Class discussions and exchange of ideas Group project and group work Laboratories
Bibliography	Systems Analysis & Design Μethods by J. Whitten, L. Bentley & K. Dittman, Fifth Edition, bMc Graw Hill Publishers. (ISBN: 0071148744) Systems Analysis and Design, 10th Edition, Harry Rosenblatt, Shelly Cashman Series (ISBN: 1285422708) Systems Analysis and Design, 6th Edition, Dennis-Wixom-Roth, Wiley (ISBN: 1118897854)
Assessment	(20 %), labs (10 %), project (20%), final exam (50 %)
Language of instruction	Greek

CSE 224 Data Structures and Algorithms

Course title	Data Structures and Algorithms
Course code	CSE 224
Course type	Mandatory
Level	Undergraduate
Year / Semester	2nd year/ 1st semester
Teacher’s name	Andreas Diavastos
ECTS	5	Lectures / week	2x2h		Laboratories / week		1x2h
Course purpose and objectives	The Data Structures and Algorithms course is an introduction to Data Structures that govern programming and to Algorithm Analysis. The aim of the course is to understand how common computational problems can be solved efficiently in programming by using appropriate data structures and algorithms. The course is taught in Python programming language. In the laboratory, students will perform programming exercises using the Python programming language, which will aim to consolidate the theory that will be delivered in the lectures.
Learning outcomes	Upon completion of the course, students will be able to: Describe and implement basic data structures and algorithms in Python Implement programming techniques that are frequently encountered in solving algorithmic problems Analyze the complexity of algorithms and estimate the computational cost of algorithms Select appropriate data structures and algorithms to solve specific programming problems Combine algorithms and data structures to solve complex computational problems.
Prerequisites	There is no strict prerequisite. Students should have good programming principles.			Required		-
Course content	The course covers the following content: Algorithm Analysis Python Programming and Object-Oriented Programming Recursion Arrays Stacks, Queues Linked Lists Trees and Binary Trees Priority Queues and Heaps Maps, Dictionaries, Hash Maps, Sets Search Trees and AVL Trees Splay Trees, (2,4) Trees Sorting and Selection Graphs and Shortest Path Algorithms
Teaching methodology	Presentations conducted by the instructor using slides, while the lab sessions, with practical programming assignments conducted in Python programming language emulator, are administered by special teaching staff under the supervision of the class instructor (assignments are prepared by the class instructor). During lectures, often an in-depth discussion with the class instructor and a questions-and-answers session is carried out. Small in-class exercises are given every week to students to help with engagement and to help them to better understand and grasp the concepts being taught. Enough time is given to students to work on solutions to these exercises and discuss them with the instructor during the lecture/lab and at the end an indicative solution is presented and explained to them.
Bibliography	Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser. Data Structures and Algorithms in Python, 1st Edition, Wiley. ISBN-13: 978-1118290279 Brad Miller and David Ranum. Problem Solving with Algorithms and Data Structures Using Python, 2nd Edition, Franklin, Beedle & Associates. ISBN-13: 978-1590282571 Kent D. Lee and Steve Hubbard. Data Structures and Algorithms with Python, With an Introduction to Multiprocessing, 2nd Edition. Springer. ISBN 978-3-031-42208-9.
Assessment	50% Final Exam: Covers the entire material taught during the semester 30% Quizzes: 3 quizzes during the entire semester, one each month of completed material. Each quiz covers only the material covered during that month. Each quiz takes 10%. 15% Lab assignments: 3 lab assignments during the entire semester, one each month. The students must deliver and present their solutions. 5% Weekly homework
Language	Greek (the class textbook is written in English, but class notes are in Greek). Some content of the class presentations, for example various technical definitions and terms, are presented in mixed-mode language, mostly in Greek (the Greek language lacks modern technical terms and hence referring to them in English is unavoidable – this is also carried out as a better preparation of students in the wider international professional scene).

CSE 225 Operating Systems

Course Title	Operating Systems
Course Code	CSE 225
Course Type	Compulsory
Level	Undergraduate
Year / Semester	2nd / 1st
Lecturer	Eliza Angelidou Loizou
ECTS	5	Lectures / week		4	Labs / week	2
Course Purpose and Objectives	The purpose of the course is to teach how a modern operating system is designed and how it operates. The double role of operating systems, as managers of system resources and as service providers is explained.
Learning Outcomes	Upon successful completion of the course the attendee should be able to: Know the historical evolution of computers and operating systems Understand how an Operating System manages the following: CPU – Processes, Main Memory, Secondary Memory – Files, Input / Output Understand memory management techniques, including paging, segmentation, and virtual memory Understand secondary storage management, including file systems, directories, and I/O management Apply operating system concepts to solve problems related to resource management and system performance
Prerequisites			Required
Course Content	The course content includes four sections: Part 1: Introduction: Introduction to basic terminology and definitions. Historical evolution of computers and Operating Systems. Basic structure, operation and services of the Operating System. The Operating System as an abstraction machine: service provider, I/O handler, error handler, process and memory manager. Part 2: Process Management: Process Definition Process scheduling Inter-process communication Communication in Client-Server Systems. Threads and Multithreading Models CPU Scheduling: Criteria and goals of process scheduling. Preemptive and non-preemptive scheduling, priorities, scheduling algorithms. Process synchronization: Racing conditions, critical regions, mutual exclusion, inter-process communication. Deadlocks: When and why deadlocks occur, examples and solutions. Part 3: Main and Virtual memory management Main memory management: Memory allocation algorithms, paging, swapping, segmentation. Virtual memory: Paging, frame allocation, thrashing. Part 4: Secondary Storage management File ManagementFile operations: creation, read/write, deletion. File Access methods, file management, memory allocation, integrity mechanisms. Directories/subdirectories. I/O Management: Character code independence, device independence, efficiency, uniform treatment of devices, buffering. Disk: Seek time, latency, rotational delay. Seek time strategies and improvement, use of cache memory.
Teaching Methods	Presentations and labs
Bibliography	• A. Silberschatz, G. Gagne, P. B. Galvin, ‘Operating Systems Concepts’, 7th edition, Wiley, 2005, ISBN 0‐471‐69466‐5. Other recommended books: • W. Stallings, 'Operating Systems: Internals and Design Principles’, Prentice Hall., 6th edition, 2009, ISBN 0‐13‐603337‐7 • A. S. Tanenbaum, A. S. Woodhull, ‘Operating Systems: Design and Implementation’, 3rd edition, 2006, ISBN 0‐13‐0‐13‐142938‐8.
Assessment	Midterm Exam (30 %), Laboratory work (20 %), Final Exam (50 %).
Language of Instruction	Greek

CSE 226 Algorithms and Complexity

Course Title	Algorithms and Complexity
Course Code	CSE 226
Course Type	Required
Level	Undergraduate
Year / Semester	2nd year / 4th semester (Spring)
Lecturer	Herodotos Herodotou
ECTS	5	Lectures / week		2 (4 hours)	Labs / week	1 (2 hours)
Course Purpose and objectives	The course introduces the fundamental principles of algorithm design and analysis, focusing on both the practical techniques for solving computational problems and the theoretical foundations of algorithmic complexity.
Learning Outcomes	At the end of the course, students will be able to: Choose correctly between different algorithms and algorithmic methodologies for solving computational problems. Design and implement new algorithms for solving computational problems using various methodologies such as divide and conquer, dynamic programming, and greedy algorithms. Analyze the complexity of the algorithms and estimate the computational cost of their software.
Prerequisites	CSE 224 Data Structures and Algorithms		Required
Course Content	The course covers the following topics: algorithm analysis, asymptotic analysis, recurrence relations, divide-and-conquer algorithms, dynamic programming, greedy algorithms, graph representation, graph search, minimum spanning trees, shortest paths, maximum flow, and NP-completeness.
Teaching Methods	Lectures, group discussions, and lab exercises
Bibliography	Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. Introduction to Algorithms. MIT Press; 4th Edition, 2022, ISBN: 9780262046305 Greek version: Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. Εισαγωγή στους Αλγόριθμους, Έκδοση 3, Πανεπιστημιακές Εκδόσεις Κρήτης, 2016, ISBN: 978-960-524-473-6.
Assessment	Midterm (30 %), homework (20 %), final exam (50 %)
Language of instruction	Greek

CSE 227 Advanced Object-Oriented Methodology and Programming (UML, Java)

Course title	Advanced Object Oriented Methodology and Programming (UML, Java)
Course code	CSE 227
Course type	Compulsory
Level	Undergraduate
Year / Semester	2nd year/4th semester
Teacher’s name	Panagiotis Ilia
ECTS	6	Lectures / week	4		Laboratories / week		2
Course purpose and objectives	The purpose of this course is to equip students with a comprehensive understanding of object-oriented programming (OOP) using the Java programming language, with a focus on both foundational principles and modern practices. The course aims to develop students’ ability to design, implement, and analyze object-oriented software systems that are robust, modular, and scalable. Through a combination of lectures, hands-on labs, and individual assignments, students will explore core concepts such as encapsulation, inheritance, and polymorphism, and learn to apply them effectively using Java. Emphasis is placed on practical problem solving, the effective use of the Java Collections Framework, proper exception handling, file I/O, and the development of graphical user interfaces. In addition, the course introduces advanced topics such as multithreading, generics, and software design patterns, preparing students for real-world development scenarios. Unified Modeling Language (UML) is used throughout the course to support object-oriented design and to strengthen students’ ability to model and communicate software architecture.
Learning outcomes	Upon successful completion of the course, students shall be able to: Understand and apply the core principles of object-oriented programming, including encapsulation, inheritance, polymorphism, and composition. Design and implement modular, maintainable applications in Java using appropriate language features and object-oriented structures. Work effectively with the Java Collections Framework and generics to manage and manipulate structured data. Handle file input/output and exceptions to build reliable and robust applications. Test and debug Java programs using appropriate tools and techniques. Develop interactive programs with graphical user interfaces and event-driven behavior. Understand and apply basic concepts of concurrent programming using threads. Use UML diagrams to support the analysis, design, and communication of object-oriented software systems.
Prerequisites	-			Required		-
Course content	The course covers fundamental and advanced concepts of object-oriented programming in Java, along with essential practices in software design and development. Java syntax, control structures, methods, arrays Classes, objects, encapsulation, composition Inheritance, polymorphism, abstraction Interfaces, abstract classes, generics Collections: lists, sets, maps, iterators, sorting Exception handling File input/output (I/O, NIO) Modularization and package management Testing and debugging Graphical user interfaces (GUI), event handling Multithreading and concurrency UML diagrams Basic design patterns Software development models
Teaching methodology	The course is delivered through a combination of lectures and supervised laboratory sessions. Lectures introduce theoretical concepts of object-oriented programming and programming techniques, supported by coding examples, class discussions, and active student engagement. Laboratory sessions focus on practical implementation, enabling students to apply object-oriented principles through guided exercises and structured programming tasks. Students receive regular feedback during lab sessions through informal assessment, helping them track their progress and improve their understanding. Teaching is supported by: Instructor-led lectures, concept demonstrations, and code examples Hands-on programming exercises in lab sessions Individual assignments and feedback UML modeling activities
Bibliography	Main References Savitch, W. Absolute Java. Ion Publishing, 2008. (ISBN: 0131365894) Deitel, H.M., & Deitel, P.J. Java Programming. Giourdas Publishing. (ISBN:0132575663) Chatzigeorgiou, A. Object-Oriented Design with UML: Principles, Patterns, and Heuristics. Kleidarithmos Publishing. Supplementary Material Savitch, W. Java: An Introduction to Problem Solving and Programming. Tziolas Publishing. Eckel, B. Thinking in Java (4th Edition). Prentice Hall. (ISBN: 0131872486)
Assessment	Student performance is evaluated through: Lab exercises: 10% Individual assignment: 20% Midterm examination: 25% Final examination: 45%
Language	Greek

CSE 228 Systems Programming

Course title	Systems Programming
Course code	CSE 228
Course type	Mandatory
Level	Undergraduate
Year / Semester	2 / Spring
Teacher’s name	Fragkiskos Papadopoulos
ECTS	5	Lectures / week	2 (4 hours)		Laboratories / week		1 (2 hours)
Course purpose and objectives	Teach multiprogramming and process/thread synchronization & communication; and provide practical understanding of Unix-based OS operation and programming in C and shells, including access to OS libraries and services. Demonstrate practical knowledge of Unix-based operating systems and the Unix file system Create and manage processes and threads, including synchronization and communication mechanisms Use inter-process communication primitives (signals, pipes) and network sockets (TCP/UDP) Apply concurrency controls such as mutexes and condition variables in C programs Write and debug shell scripts and filters; employ regular expressions for text processing
Learning outcomes	Basic knowledge of operation and programming of Unix-based operating systems. Κnowledge of multiprogramming.
Prerequisites	-			Required		-
Course content	Introduction to Unix; the Unix file system; processes and threads: creation, synchronization, communication, signals; pipes; TCP/UDP sockets; mutexes; condition variables; regular expressions; shell scripts; filters.
Teaching methodology	Lectures, discussions, running code illustrations and examples, solutions to exercises.
Bibliography	“Τhe Unix Programming Environment”, Brian W. Kernighan and Rob Pike. Prentice Hall (March 1984). (ISBN: 013937681X) “Sams Teach Yourself Unix in 24 Hours”, Dave Taylor and James C. Armstrong. Pearson Education, 2005. (ISBN: 0672314800)
Assessment	Programming assignments (40%), final exam (60%).
Language	Greek

CSE 229 Bioinformatics

Course title	Bioinformatics
Course code	CSE 229
Course type	Mandatory
Level	Undergraduate
Year / Semester	2nd year/4th semester
Teacher’s name	Christos Loizou
ECTS	6	Lectures / week	2x2h		Laboratories / week		-
Course purpose and objectives	Students should assimilate the necessary theoretical and practical knowledge about the various aspects of bioinformatics. Introduction to Biomechanics and Bioinformatics. Statistical significance of the alignment results. Searching databases for similar sequences, efficiency of related algorithms. Statistical analysis of DNA microarray experimental data. Special topics include, biomechanics and Biomaterials, bioprocessing and telemedicine, biomedical instrumentation, signal processing and imaging modalities. Introduction to the emerging medical device industry. Imaging with ultrasound, X-ray, CT, MRI, and positron emission tomography (PET). Role of laser beams in surgery. Endoscopy and endoscopy-assisted treatments. Cardiac devices (e.g. pacemakers). Principles of the nervous system. Understanding of the electrocardiogram, electroencephalogram and electromyogram. Principles of the nervous system. Understanding of the electrocardiogram, electroencephalogram and electromyogram. Principles of radiotherapy, telematics in health care. Therapeutic ultrasound and applications of therapies using thermal radiation. Pairwise sequence alignment and multiple sequence alignment algorithms. Protein classification and structure prediction.
Learning outcomes	Gain knowledge of the basic principles of biotechnology and engineering in biology and medicine Understand the basic functions of the human body and how biological signals are generated Apply engineering principles to biological signal retrieval and biomedical problems Understand and apply sequence alignment algorithms, phylogenetic prediction, and protein classification Analyze DNA microarray data and evaluate statistical significance of bioinformatics results Understand and apply principles of medical imaging (ultrasound, X-ray, CT, MRI, PET) Demonstrate knowledge of telemedicine, biomaterials, and biosensors
Prerequisites	-			Required		-
Course content	The following chapters are covered: Module 1: Biomedical signals, systems and ultrasound basics. Module 2: Introduction to Biostatistics and Statistical Data Analysis, Biomedical Health Systems. Module 3: Health Information Systems, MRI and DICOM & PACS standard. Module 4: Medical Imaging (Processing, Analysis, Quality Assessment), technologies and applications. Digital radiography. Module 5: Human Brain, Brain Anatomy, Nervous System (CNS), X-rays. Module 6: Endoscopy, Heart and function and diagrams, Electrocardiogram, Pressure and function. Module 7: Medical Machines, EEG, Blood Pressure, Blood Pressure, Radiation devices. Module 8: Telemedicine, technologies and applications. Module 9: Biomaterials. Module 10: Biosensors, and statistical data analysis. Why do we need bionics? Module 11: 3D Printing, Introduction to molecular biology, analysis/representation. Natural diversity and variation. Module 12: Thermal Ablation, Treatment, Techniques.
Teaching methodology	Lectures. Every week, the course instructor will present an introduction to the topic in the form of a lecture presentation. The course will be a blend of presentations, problem solving, group discussions, example use, Matlab programming, and laboratory exercises.
Bibliography	E. Shortliffe, L Perreault et. al, “Medical Informatics COMPUTER Applications in Health Care and Biomedicine”, Springer-Verlag, New York, 2001, ISBN 0-387-98472-0. J.H van Bemmel, M.A. Musen, “Medical Informatics”, Springer-Verlag, 2000, ISBN 3-450-63351-0. "Βιοπληροφορική, Δυνατότητες και Προοπτικές", Σοφία Κοσσίδα "Bioinformatics- From Genomes to Drugs", Edited by Thomas Lengauer, Volumes I & II, WILEY-VCH, 2001. "Fundamental Concepts of Bioinformatics", D. Krane and M.L.Raymer, Pearson Education, 2003. "Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins", Edited by A.D.Baxevanis and B.F.F.Ouellette, Wiley-Liss, Inc., 2001 Further additional reading is given below: Class notes and Research Articles.
Assessment	Lab exercises and projects (10%) Midterm Examination (40%) Final exam (50%)
Language	Greek

CSE 321 Computer Networks and Communications

Course title	Computer Networks and Communications
Course code	CSE 321
Course type	Mandatory
Level	Undergraduate
Year / Semester	3 / Fall
Teacher’s name	Fragkiskos Papadopoulos
ECTS	5	Lectures / week	2 (4 hours)		Laboratories / week		-
Course purpose and objectives	Introduction to computer networks, focusing on the Internet. The course covers a wide range of topics, such as application layer protocols (e.g., Web, FTP, DNS), content distribution, client-server architectures and peer-to-peer architectures, data transport protocols TCP/UDP, reliable data transfer and congestion control principles, Internet congestion control, Internet routing principles, the IP protocol and network router architectures, Internet addresses, IPv4 and IPv6, wired and wireless access networks (e.g., Ethernet, DSL, WLANs), data link protocols.
Learning outcomes	Explain Internet architecture, protocol layering, and performance measures Analyze and apply transport-layer mechanisms including TCP/UDP, reliable data transfer, and congestion/flow control Describe and configure network-layer functions: IP addressing (IPv4/IPv6), routing principles, and router architecture basics Explain data link layer protocols and wired/wireless access networks (e.g., Ethernet, DSL, WLANs) Apply application-layer concepts (Web/HTTP, FTP, DNS) and client-server and peer-to-peer architectures
Prerequisites	-			Required		-
Course content	Introduction to computer networks, Internet structure, protocol layers, performance measures, basic concepts. Data link layer, local area networks. Network layer, IP protocol, routing, addresses. Transport layer, TCP/UDP protocols, congestion and flow control. Application layer, Client-Server and Peer-to-Peer architectures.
Teaching methodology	Lectures, discussions, solutions to exercises.
Bibliography	Computer Networking: A Top-Down Approach, by James F. Kurose & Keith W. Ross. (ISBN: 9780133594140)
Assessment	Midterm (40%), assignments (5%), final exam (55%)
Language	Greek

CSE 322 Computer Networks and Communications Laboratory

Course title	Computer Networks and Communications Laboratory
Course code	CSE 322
Course type	Mandatory
Level	Undergraduate
Year / Semester	3 / Fall
Teacher’s name	Special teaching staff
ECTS	4	Lectures / week	-		Laboratories / week		1 (4 hours)
Course purpose and objectives	This course aims at the practical training and experimentation of students in computer networking problems. In particular, the course offers students the ability to connect local computer networks using switches, routers and hosts, and perform various experiments related to the TCP/IP protocol stack.
Learning outcomes	Acquiring practical experience with IP addressing, subnetting, and subnet masks Understand and apply routing protocols such as RIP, OSPF, and BGP Configure and manage multiple access protocols using routers, switches, and hosts Program and configure routers using Cisco IOS Perform TCP and UDP experiments and measurements on real networks
Prerequisites	-			Required		CSE 321 – Computer Networks (during the same semester)
Course content	Introduction to the laboratory hardware and software, the Cisco operating system (Cisco IOS). Single segment IP networks. Static routing. Dynamic routing (RIP, OSPF, BGP). UDP and TCP experiments and measurements on real networks.
Teaching methodology	Solution of a series of laboratory exercises using the laboratory equipment under the guidance of the instructor. Presentations and group discussions.
Bibliography	Mastering Networks: An Internet Lab Manual, by Jörg Liebeherr and Magda El Zarki, Pearson/Addison Wesley (2004). (ISBN: 0201781344)
Assessment	Laboratory exercises (50%), final exam (50%).
Language	Greek

CSE 323 Computer Architecture

Course title	Computer Architecture
Course code	CSE 323
Course type	Mandatory
Level	Undergraduate
Year / Semester	3rd year/5th semester
Teacher’s name	Andreas Diavastos
ECTS	6	Lectures / week	2x2h		Laboratories / week		1x2h
Course purpose and objectives	The course provides a comprehensive introduction to the fundamentals of computer architecture, focusing on the design and behavior of computer systems. The course answers the “What” and “Why” of a computer system and focuses on the various techniques that increase its performance. The course emphasizes the relationship between architecture and software performance, as well as current trends and challenges in computer architecture research. The course teaches Instruction-level Parallelism (ILP), Data-level Parallelism (DLP), and Thread-level Parallelism (TLP). It also teaches the latest technologies such as Cloud Computing (at the architecture level) and the latest Domain-specific Accelerators, including GPU and AI accelerators. Each module will examine the most important techniques used today in existing systems as well as in research projects.
Learning outcomes	Upon completion of the course, students will be able to: Understand and apply modern methodology for evaluating and comparing the performance of computer systems. Describe and explain the basic and advanced principles that govern the architecture of different types of modern processors and their impact on performance. Evaluate current trends and challenges in computer architecture. Answer the questions "What" and "Why" a computer system implements to increase its performance.
Prerequisites	EEI233 - Computer Organization			Required		-
Course content	The course covers the following content: Basic principles of quantitative design and analysis of computer systems Parallelism at the instruction level Branch Prediction Dynamic Scheduling Hardware Speculation Parallelism at the data level GPUs Parallel systems Multiprocessors Shared memory and Cache coherence Memory Consistency Parallelism at the request level Cloud Computing Special Accelerators Domain-specific Accelerators AI and DNN Accelerators Additional research/practical topics based on the evolution of technology (e.g. Processor Security, Quantum Computing)
Teaching methodology	Presentations are conducted by the instructor using slides, while in the lab, material is applied on the basis of the class project using simulators and processor design tools by specialized teaching staff under the supervision of the instructor (the project is prepared by the instructor). The project lasts the entire semester. During the lectures, an in-depth discussion with the instructor is often held with questions and answers. Each week, small exercises are given in class to the students to help them better understand the concepts being taught. Students are given enough time to work on possible solutions to these exercises and discuss them with the instructor during the lecture/workshop and at the end, an indicative solution is presented and explained to them.
Bibliography	John L. Hennessy and David A. Patterson, Computer Architecture: A Quantitative Approach, 6th Edition, Morgan Kaufman. ISBN: 978-0-12-383872-8 David A. Patterson and John L. Hennessy, Computer Organization and Design: The Hardware/Software Interface, 3rd or 4th Edition, Morgan Kaufman. (ISBN: 0123747503)
Assessment	50% Final Exam: Covers the entire material taught during the semester 20% Quizzes: 2 quizzes during the entire semester. Each quiz covers only the material covered between quizzes. Each quiz takes 10%. 30% Team Project: The students must work in teams and address the requirements of the project during the entire semester. The students must deliver and present their solutions at the end of the semester.
Language	Greek (the class textbook is written in English, but class notes are in Greek). Some content of the class presentations, for example various technical definitions and terms, are presented in mixed-mode language, mostly in Greek (the Greek language lacks modern technical terms and hence referring to them in English is unavoidable – this is also carried out as a better preparation of students in the wider international professional scene).

CSE 324 Software Engineering

Course Title

Software Engineering

Course Code

CSE 324

Course Type

Compulsory

Level

Undergraduate

Year / Semester

3rd / 5th

Teacher’s Name

Andreas Andreou

ECTS

Lectures / week

2 (4 hours)

Laboratories / week

1 (2 hours)

Course Purpose and Objectives

This course is an introduction to Software Engineering, a branch of Computer Science that deals with the theoretical approaches, methodologies and tools required to develop quality software systems within the agreed schedule and budget. To enable students to absorb the necessary theoretical knowledge about the various phases of software development, study the associated complexity and related problems, and identify the techniques and models available to address them.

Learning Outcomes

Identify and distinguish the steps of the various phases of the software development life cycle
Specify, design, and implement software systems using appropriate methodologies and tools
Understand and apply software project management concepts including team organization and planning
Use CASE tools for requirements analysis, design, implementation, and testing
Apply testing, implementation, integration, and maintenance techniques in software systems

Prerequisites

Required

Course Content

The following chapters are covered:

Introductory concepts
The software development process
Software life cycle models
Team organization and management of software projects
CASE Tools: Computer Assisted Software Engineering
Testing
Introduction to Objects
Requirements
Specifications
Object-oriented analysis
Design
Implementation
Implementation and integration
14. Maintenance

Teaching Methodology

Lectures, presentations, group discussions, examples and use cases, and laboratory demonstrations

Bibliography

“Classical and Object-Oriented Software Engineering with UML and Java”, Stephen R. Schach, McGraw-Hill (5th ed., 2002 or more recent) (ISBN: 0073376183)
“Software Engineering”, Ian Sommerville, Addison-Wesley (7th ed., 2004 or more recent). (ISBN: 0133943038)
“The Unified Software Development Process”, Ivar Jacobson and Grady Booch and James Rumbaugh, Addison-Wesley, Object Technology Series, 1999, or more recent. (ISBN: 0321245628)
“Software Project Management: A Unified Framework”, Walker Royce, Addison-Wesley, Object Technology Series, 1998, or more recent. (ISBN: 0201309580)
Research articles.

Assessment

Laboratory Exercises	10%
Article Presentation	10%
Semester project	15%
Mid-term examination	20%
Final examination	45%

Language

Greek

CSE 325 Databases

Course Title	Database Systems
Course Code	CSE 325
Course Type	Required
Level	Undergraduate
Year / Semester	3rd year / 5th semester (Fall)
Lecturer	Herodotos Herodotou
ECTS	5	Lectures / week		2 (4 hours)	Labs / week		1 (2 hours)
Course Purpose and objectives	This course introduces the principles and practices of database systems. It covers the theoretical foundations of relational databases, practical skills for designing and querying databases, and insight into how modern database management systems (DBMS) operate.
Learning Outcomes	At the end of the course, students will be able to: Design an effective and efficient database Execute SQL queries in a database for data insertions, updates, deletions, extraction, and analysis Manage a database properly according to application requirements.
Prerequisites	-		Required			-
Course Content	The course provides a solid database background focusing on relational database management systems. Topics include data modeling, database design theory and methodology, entity-relationship model, normalization, data definition and manipulation languages (SQL), storage and indexing techniques, and transaction processing. The course also covers fundamentals of database management systems architecture and methods for database application development.
Teaching Methods	Lectures, group discussions, and lab exercises
Bibliography	Abraham Silberschatz, Henry F. Korth, S. Sudarshan. Database System Concepts. 7th Edition, McGraw-Hill, 2019, ISBN: 9780078022159. Greek version: Συστήματα Βάσεων Δεδομένων, Η Πλήρης Θεωρία των Βάσεων Δεδομένων, 7η Έκδοση, Μ. Γκιούρδας, 2019
Assessment	Midterm (30 %), homework (24 %), final exam (46 %)
Language of instruction	Greek

CSE 326 Web Engineering

Course Title	Applied HCI with Generative AI
Course Code	CSE 326
Course Type	Compulsory
Level	Undergraduate
Year / Semester	3rd / 6th
Lecturer	Michalis Sirivianos
ECTS	6	Lectures / week		2 (2 hours)	Labs / week		2
Course Purpose and objectives	Applied HCI with Generative AI is a studio-style course that grounds students in the principles of human–computer interaction (user‑centered design, cognition, accessibility, and ethics), while using modern generative tools to accelerate ideation, prototyping, UX writing, and implementation. Students will design and build responsive web and (optionally) mobile interfaces with React, guided by evidence from usability testing and analytics, and will learn to balance AI assistance (Cursor, Figma/Framer AI) with rigorous human judgment. The course emphasizes design systems, accessibility (WCAG 2.2), privacy‑by‑design, and reproducible collaboration, culminating in a research‑backed case study and a deployable MVP.
Learning Outcomes	• Plan and run user research; synthesize insights into personas, journey maps, and task flows. • Produce low- to high-fidelity prototypes. • Write clear UX copy, microcopy, and error states. • Implement interactive UIs for web. • Use AI assistance responsibly in the IDE for velocity with code review and tests. • Ensure accessibility and inclusivity. • Evaluate designs empirically. • Apply design tokens and component libraries for consistency and scale. • Run safe deployments and experiments. • Reflect ethically on AI-assisted design—bias, privacy, transparency, and consent.
Prerequisites	-		Required			-
Course Content	Week 1 — HCI foundations & AI-assisted workflow HCI history, user-centered design, double diamond; overview of generative tools. Tools: FigJam, Cursor, Figma AI Week 2 — Research methods & task analysis Interviewing, surveys, diary studies, jobs-to-be-done; personas & journey maps. Tools: FigJam/Miro, Google Forms/Typeform, LLM prompts in Cursor Week 3 — Information architecture & flows Site maps, task flows, cognitive walkthroughs, card sorting. Tools: OptimalSort (or FigJam), Whimsical Week 4 — Sketching & low-fidelity prototyping Paper → wireframes; heuristic evaluation (Nielsen’s 10). Tools: Figma (wireframes), Figma AI for variations Week 5 — Visual design systems Design tokens, grids, typography, color, elevation; design libraries. Tools: Figma variables, Style Dictionary, Material 3 Week 6 — High-fidelity, motion & micro-interactions Component design, states, transitions; motion for feedback/affordance. Tools: Figma prototyping, Framer/Framer AI, Lottie Week 7 — Accessibility & inclusive design WCAG 2.2, semantics, contrast, keyboard/screen reader, localization. Tools: axe DevTools, Lighthouse, NVDA/VoiceOver/TalkBack Week 8 — Front-end foundations (web) React + Next.js, Tailwind, design–dev handoff; Storybook. Tools: React, Next.js, Tailwind CSS, Storybook, Git/GitHub Week 9 — Mobile foundations (React Native) Platform patterns and device testing; parity with web components. Tools: React Native, Expo, iOS/Android emulators Week 10 — AI-assisted implementation & UX writing Prompt patterns, refactoring, tests; code reviews for safety. Tools: Cursor with LLMs, ESLint, Prettier, Jest, React Testing Library Week 11 — Measurement, analytics & experiments Usability studies, funnels, A/B tests; interpreting evidence. Tools: PostHog or umami, Maze or Lookback, Vercel feature flags Week 12 — Ethics, privacy, and data-in-design GDPR, AI Act, DSA, DPIA, consent flows, dark-pattern avoidance; model risks & disclosures.cTools: DPIA templates, consent pattern libraries Week 13 — Final demo & case study Usability re-test, accessibility audit, polish & deploy; write up results.cTools: Lighthouse/axe reports, Vercel/Netlify, Figma/Notion
Teaching Methods	Slides, notes and online tutorials to be delivered by the tutor.
Bibliography	Textbooks: Interaction Design: Beyond Human–Computer Interaction (Rogers, Sharp, Preece), latest edition. (ISBN: 111990109X) The Design of Everyday Things (Revised & Expanded) — Don Norman. (ISBN: 9780465050659) Designing Interfaces (3rd ed.) — Jenifer Tidwell, Charles Brewer, Aynne Valencia. (ISBN: 1492051969) About Face: The Essentials of Interaction Design (4th ed.) — Cooper, Reimann, Cronin, Noessel. (ISBN: 1118766571) Don’t Make Me Think, Revisited — Steve Krug. (ISBN: 9780321965516) Online Resources: Nielsen Norman Group — research-based UX guidance. W3C WCAG 2.2 — accessibility success criteria & techniques. Apple Human Interface Guidelines; Google Material Design 3. Figma & Figma AI docs; Framer/Framer AI tutorials. React (react.dev), Next.js (learn pages), Tailwind CSS (docs). React Native (reactnative.dev) & Expo (docs.expo.dev). Deque University & axe DevTools documentation. PostHog docs; Vercel/Netlify deploy guides.
Assessment	Homework/LabTasks (65%), Final Project Presentation (15%), Final Exam (20%)
Language of instruction	Greek

CSE 327 Parallel Processing and Distributed Systems

Course Title	Parallel Processing and Distributed Systems
Course Code	CSE 327
Course Type	Compulsory
Level	Undergraduate
Year / Semester	3rd / 6th
Teacher’s Name	Michael Sirivianos
ECTS	6	Lectures / week		2 (2 hours)	Laboratories / week		-
Course Purpose and Objectives	The course aims to introduce students to the design, algorithms, hardware and software of modern distributed systems, emphasizing both efficiency and resilience and security. Topics covered include architectures of distributed systems, processes, inter‑process communication, naming in distributed entities, coordination, distributed agreement in fault‑prone environments, and security. Distributed systems and parallel processing computing form the basis of advanced technologies such as cloud computing, blockchain and artificial intelligence systems. They are applied in sectors such as finance, e‑commerce, entertainment and healthcare, offering solutions to real‑world problems, such as efficiency, reliability and fault tolerance. Knowledge of this subject is essential for jobs in software engineering, computer systems design, DevOps, and MLOps.
Learning Outcomes	At the end of the course, students: Will gain knowledge related to the basic design of distributed systems and ways of achieving their fundamental functions such as distributed consensus. Will acquire deep technical knowledge of when and how to leverage the capabilities of modern parallel and distributed systems to serve the needs of their organizations. They will become familiar with big data processing systems like those used by companies such as Google, Amazon, Meta, Microsoft, etc. Will be trained to critically understand controversial issues so as to distinguish genuinely scientific information from marketing nonsense. For example, why do some people claim that centralized solutions are something we never want? How useful is blockchain technology after all?
Prerequisites	-		Required			Basic programming knowledge in C, Java or Python. Basic concepts of Operating Systems and Computer Networks. Excellent understanding of technical English texts.
Course Content	Architectures of Distributed Systems Processes Communication Naming Coordination and Distributed Consensus Consistency and Replication Fault Tolerance Security
Teaching Methodology	Each week the instructor presents an introduction to the topic by analyzing distributed computing problems and solving them through a dialectical approach in class. Weekly lab lectures where the instructor interacts with the students while they write software. Three programming assignments in Python leading to the completion of a semester–long project. We mainly use the packages grpcio and grpcio‑tools for RPC, the requests package for HTTP and the Flask Python Web Framework. For the database we use SQLlite. We also use Docker for containerization and GitHub for versioning.
Bibliography	M. van Steen and A.S. Tanenbaum, Distributed Systems, 4th edition, 2023. (ISBN: 9081540637) https://www.distributed-systems.net/index.php/books/ds4/ Research articles on distributed systems distributed by the instructor.
Assessment	Class Participation: 10%. Attendance is mandatory. In each class, students who are present receive 0.2 of a point out of 100 for their attendance and another 0.2 of a point for their participation with questions, answers and critical discussions. For example, assuming that we have 25 lectures, a student who is present in all classes but does not participate at all receives 25 x 0.2 = 5%. Programming Assignments: 30%. Three programming assignments are assigned during the semester. The assignments are posted on the course page on Moodle with strict deadlines. The submission of assignments is made exclusively in electronic form through Moodle. The assignments are judged based on their correctness in execution, their design and their structure. For each requirement of the assignment, unit tests will be provided to students for checking the correctness of the solution. Assignments that do not pass the unit test receive zero. Final Exam: 60%. The exam is conducted in written form with open electronic sources consisting of the course slides, recommended notes and/or articles and the book. The sources must be stored in a folder called CSE 327. The files must have exactly the same name as they have on Moodle. The exam covers the entire syllabus. A student who does not achieve 30% out of the 60% in the final exam fails the course. Plagiarism, use of the Internet, and use of Artificial Intelligence during the exam is strictly prohibited. Participants in plagiarism or use of AI and Internet will be subject to a zero grade in the course and referral to the Disciplinary Committee.
Language	Greek

CSE 328 Software Engineering Project and Professional Practice

Course Title	Software Engineering Project and Professional Practice
Course Code	CSE 328
Course Type	Compulsory
Level	Undergraduate
Year / Semester	3rd / 6th
Teacher’s Name	Andreas Andreou
ECTS	7	Lectures / week		2 (4 hours)	Laboratories / week		1 (2 hours)
Course Purpose and Objectives	Students shall apply in practice the necessary theoretical knowledge about the various stages producing complete software systems through the development of a large system in groups.
Learning Outcomes	Apply practically all stages and phases of analysis, specification, design, and implementation of a large software system Produce complete project documentation including requirements, specifications, design, implementation, and manuals Collaborate effectively within a development team, applying project management practices and monitoring progress Present and defend a completed software system before stakeholders through presentation and oral examination Perform client training, system delivery, and first maintenance cycle, including detection and correction of problems
Prerequisites	CSE 324 – Software Engineering		Required			-
Course Content	This course is a natural follow-up to CEI 324 - Software Engineering and aims to assimilate and put to practice all theoretical approaches and methodologies used to develop quality software systems, through teamwork by developing a specific software product to serve specific needs of a customer in the local market and society, as well as the academic / research area. The work performed is monitored by the instructor through weekly meetings with each group separately (instead of lectures), during which issues related to the development process are discussed and analyzed, various related problems are presented and solved, while alternative strategies are considered. In each meeting, the teams deliver a progress report detailing the work carried-out since the previous meeting, monitoring the progress of the project based on the original schedule and Gantt chart, outlining the problems encountered and other issues to be discussed during the meeting. This document also contains a brief description of the work carried out by each member of the team, as well as the tasks to be undertaken and completed by the next meeting. During the implementation of the systems, metrics are recorded regarding the effort, cost, time, development process and software quality. Each group documents and delivers the following: Requirements Analysis Document Technical Specifications Document Design Document Implementation Document Installation and Operation Manual Finally, the team trains the client and performs a first system maintenance / support cycle for a one-week period during which it detects and corrects any problems and errors arise, and records future extensions of the software system developed.
Teaching Methodology	Meetings, software system development problem analysis, guidance and mentoring, group discussion, examples and use cases, and laboratory demonstrations
Bibliography	“Classical and Object-Oriented Software Engineering with UML and Java”, Stephen R. Schach, McGraw-Hill, 4th ed., 1999, or newer. (ISBN: 0073376183) “Software Engineering”, Ian Sommerville, Addison-Wesley, 7th ed., 2001, or newer. (ISBN: 0131215620) “The Unified Software Development Process”, Ivar Jacobson and Grady Booch and James Rumbaugh, Addison-Wesley, Object Technology Series, 1999, or newer. (ISBN: 0201571692) “Software Project Management: A Unified Framework”, Walker Royce, Addison-Wesley, Object Technology Series, 1998, or newer. (ISBN: 0201309580) “The Unified Modeling Language User Guide”, Grady Booch, Ivar Jacobson and James Rumbaugh, Addison-Wesley, Object Technology Series, 1998, or newer. (ISBN: 0201571684) “Software Engineering, A Practitioner’s Approach, European Adaptation”, Roger S. Pressman, 5th Edition, McGraw-Hill, 2000, or newer. (ISBN: 9780077096779) Manuals of software tools necessary for each teamwork.
Assessment	Each completed project is publicly presented before an audience (instructor and stakeholders) followed by a cloded session in which an oral examination of each person in the group is performed, with questions covering the whole project and not only parts in which she/he was actively involved. Evaluation is based on the level of completion and quality of the software system (45%), how the team members collaborated during system development (10%), the quality of the documentation (20%), the progress reports (5%), the presentation of the system (5%) and the oral examination (15%).
Language	Greek

CSE 329 Statistical Pattern Recognition and Machine Learning

Course Title	Statistical Pattern Recognition and Machine Learning
Course Code	CSE 329
Course Type	Theory, Lab
Level	BSc
Year / Semester	3rd year/2nd semester
Teacher’s Name	Dr. Sotirios Chatzis
ECTS	6	Lectures / week		4h	Laboratories / week		2h
Course Purpose and Objectives	The course is an introduction of core concepts in modern artificial intelligence. We first introduce the concepts of supervised, unsupervised, and semi-supervised learning. Subsequently, we present the main biological inspiration of modern unsupervised data representation, namely Deep Learning. Further, we introduce basic supervised learning models. In addition, we elaborate on Collaborative Filtering algorithms, with emphasis on Matrix Factorization. We present the problems of multiple instance learning and semi-supervised, as they are treated in the context of the large margin framework. In all cases, we elaborate on techniques we can use to prevent overfitting, by means of appropriate use of regularization methods. Finally, we introduce reinforcement learning techniques, where agents learn optimal policies through dynamic interaction with their environment.
Learning Outcomes	Discern among the main types of machine learning approaches (supervised, unsupervised, semi-supervised, reinforcement) Understand the pros, cons, and application areas of different machine learning models Apply regression, classification, clustering, and validation techniques to solve real-world problems Use feature learning and collaborative filtering techniques including matrix factorization Apply reinforcement learning techniques to dynamic environments Perform hands-on experiments with classification, regression, and clustering tasks using scikit-learn
Prerequisites	-		Required			-
Course Content	The module comprises a theoretical part and a series of laboratory exercises. THEORETICAL PART: Introduction Elements of Machine Learning Regression, Classification Validation, Model Selection Clustering Feature Learning LABORATORY: Solving simple classification, regression, and clustering tasks using Scikit learn.
Teaching Methodology	Lectures. Every week, the course instructor will present an introduction to the topic in the form of a lecture presentation. Lab. The lab practical will consist of hands-on experimentation on solving real-world data science problems.
Bibliography	Representative readings Vishal Maini, Samer Sabri, Machine Learning for Humans. (ISBN: 973234668X) Sergios Theodoridis, Machine Learning: A Bayesian and Optimization Perspective. Academic Press, 2015. (ISBN: 0128015225) David Barber, Bayesian Reasoning and Machine Learning. Cambridge University Press. (ISBN: 0521518148) Christopher M. Bishop, Pattern Recognition and Machine Learning. Springer. (ISBN: : 0-387-31073-8)
Assessment	Interim exam (50%) Final exam (50%)
Language	Greek

CSE 423 Introduction to Cryptography and Computer Security

Course title	Introduction to Cryptography and Computer Security
Course code	CSE 423
Course type	Compulsory
Level	Undergraduate
Year / Semester	4rd year/7th semester
Teacher’s name	Panagiotis Ilia
ECTS	6	Lectures / week	4		Laboratories / week		-
Course purpose and objectives	This course aims to provide a thorough introduction to the foundational principles and core methodologies of cryptography and computer security. It is designed to equip students with the theoretical background and practical insights necessary to understand, implement, apply, and evaluate security practices and mechanisms in modern computing environments. The primary objective is to familiarize students with the essential concepts that underpin the design and implementation of secure systems, including both classical and modern approaches to cryptographic techniques and threat mitigation. Emphasis is given on developing the ability to reason critically about security requirements, understand threats, assess risks, and apply suitable cryptographic tools, mechanisms, and strategies to protect computing systems. In addition to the core material, the course offers a brief introduction to advanced topics such as post-quantum cryptography and homomorphic encryption, primarily to raise awareness of emerging challenges and developments in the field. The course also touches upon foundational privacy-related concepts within the broader context of security.
Learning outcomes	Upon successful completion of the course, students will be able to: Understand the fundamental principles of computer and network security, including the core security goals of confidentiality, integrity, and availability. Describe and compare essential cryptographic primitives, such as symmetric and asymmetric encryption, hash functions, message authentication codes (MACs), and digital signatures. Apply cryptographic protocols, including key exchange and digital certificates (PKI), within the context of secure communication, for secure system design. Demonstrate awareness of selected advanced topics, such as post-quantum cryptography and homomorphic encryption. Identify and analyze common security threats and attack vectors, including malware (e.g., viruses, worms, Trojan horses), spoofing and phishing, denial-of-service attacks, cross-site scripting (XSS), SQL injection attacks, and buffer overflow vulnerabilities. Recognize evolving security threats and system vulnerabilities, and design secure systems/applications by selecting and applying appropriate defense strategies, including authentication, authorization, and access control. Evaluate alternative cryptographic techniques and security mechanisms, considering trade-offs, performance, complexity, usability, and the value of protected assets. Discuss basic privacy concerns in computer systems and understand foundational privacy-preserving practices.
Prerequisites	-			Required		-
Course content	The course provides an introduction to the foundational concepts and techniques of cryptography and computer security. It begins with the basic principles and objectives of security, including confidentiality, integrity, availability, as well as authentication, non-repudiation, and accountability. It also provides an overview of security design principles and classical security models. Students are introduced to risk assessment and threat modeling techniques as a foundation for evaluating system vulnerabilities and mitigating potential attacks. Core cryptographic topics include symmetric and asymmetric encryption, hash functions, pseudorandom generators, stream ciphers, message authentication codes (MACs), and digital signatures. The course also covers key management practices and cryptographic protocols, including secure key exchange and the use of digital certificates within Public Key Infrastructures (PKI). Fundamental authentication and access control models and mechanisms are explored, along with models for authorization and identity management. The course addresses software and system vulnerabilities, including denial-of-service attacks, malware (e.g., viruses, worms, Trojans), phishing, spoofing, and common web security issues such as cross-site scripting (XSS), SQL injection, and buffer overflow attacks. Students learn secure coding practices and principles for reducing software-based risk, as well as browser security mechanisms such as the Same-Origin Policy. The course also examines key defense mechanisms used in practice, including antivirus software, firewalls, intrusion detection systems (IDS), and intrusion prevention systems (IPS). Real-world security protocols such as TLS/SSL are introduced, alongside password-based authentication, password management and storage, and multi-factor authentication mechanisms. The course also explores social engineering techniques, human factors in security, and the challenges of balancing security with practicality and usability. Finally, the course includes a brief overview of selected advanced topics in cryptography, such as post-quantum cryptography, homomorphic encryption, and lightweight cryptographic methods, and introduces students to privacy-related concepts and privacy-preserving mechanisms and practices.
Teaching methodology	Presentations, Attack & Defense Demonstrations, Group Discussions and Project
Bibliography	Panagiotis Ilia Educational material Katos V., Stephanidis G., Cryptography and Cryptanalysis Techniques, Zygos, 2003 Stallings W., Brown L., Computer Security: Principles and Practice, 3rd Edition, Pearson, 2014 (ISBN: 0133773922) Stallings W., Network Security Essentials: Applications and Standards, 6th Edition, Pearson, 2016 (ISBN: 9789352866601) Pfleeger C. P., Pfleeger S. L., Security in Computing, 4th Edition, Prentice Hall, 2008 (ISBN: 0132390779) Schneier B., Applied Cryptography: Protocols, Algorithms, and Source Code in C, 2nd Edition, John Wiley & Sons Inc, 1996 (ISBN: 9780471117094) Burmester M., Gritzalis S., Chrysikopoulos V., Katsikas S., Modern Cryptography: Theory and Applications, Papasotiriou, 2011
Assessment	Midterm Exam (30%), Projects (20%), FinalExamination (50%)
Language	Greek

CSE 461 Machine Learning Systems at Scale

Course title

Machine Learning Systems at Scale

Course code

CSE 461

Course type

Elective

Level

Undergraduate

Year / Semester

4th Year / 1st Semester

Teacher’s name

Michael Sirivianos

ECTS

Lectures / week

1 lecture in the first half of the semester and 2 lectures in the second half(3 hours per lecture)

Laboratories / week

Course purpose and objectives

The course delves into the principles of designing and implementing distributed and networked operating systems for Machine Learning (ML). It emphasizes classical operating system design principles and describes the implementation of modern systems that form the backbone of big data processing and storage infrastructure in cloud computing and AI. The course content includes the following topics: Virtual Memory, Process Management, and Inter-process Communication. The Unix operating system. Log-structured file systems. Distributed file systems. Virtual Machines. Stochastic Gradient Descent and Attention at Scale. Topics covering the lifecycle of Data Analysis for ML, including Data Sourcing and Preparation for ML, ML programming models, and Distributed Computing Frameworks.

Learning outcomes

By the end of the course, students will be able to:

Describe the major design principles of distributed operating systems.
Understand and implement key techniques for large-scale distributed systems.
Comprehend the functioning of cloud computing and ML infrastructure from companies like Google, Amazon, OpenAI and Meta.
Read and understand research papers on massively parallel computer systems.
Use, modify and evaluate complex large-scale distributed systems for Machine Learning.

Prerequisites

Undergraduate subjects in the fields of Operating Systems, Computer Networks and Machine Learning.

Required

Course content

Week 1: Process and Memory Management: The Unix Time Sharing System
Week 2: Storage Systems: Unix File System, Log-structured File System, WiscKey. Virtualization: Xen
Weeks 3-4: Infrastructures for Big Data (Google): Google File System, Bigtable, Borg/Kubernetes
Week 5: ML Lifecycle: Data Management in ML, MLOps
Weeks 6-7: Classical ML Training at Scale: Stochastic Gradient Descent with Back-propagation, Parameter Server, XGBoost. Spark
Weeks 8-10: Deep Learning Systems: TensorFlow, Pytorch Distributed, Multi-head Attention
Week 11: System-assisted Feature Engineering and Model Selection: Cerebro, Model Selection Management Systems
Week 12: Data Sourcing and Preparation: Tensorflow Data Validation, Snorkel. ML Deployment: Pathways
Week 13: Reinforcement Learning: Ray. Federated Learning: Oort. LLMs: Parrot, DiffKV. Agents: LangChain, LangGraph, LlamaIndex

Teaching methodology

Weekly lectures with presentations of research articles with slides and notes distributed by the lecturer. Oral questions on the articles and student responses in the classroom. Research article presentations by students. Discussions that promote reflection and critical thought, after assigning students to write two-page summary papers, solve quizzes and present articles. AI-proof written examinations with open printed notes.

Bibliography

Main Reading Material:
- Research articles that are available on Moodle.
- Notes and slide-decks on taught material that are available on Moodle.

Optional Reading Material:
- “Operating Systems Concepts” (book) by Silberschatz, Galvin, and Gagne (ISBN: 1119800366)
- Book slides at http://codex.cs.yale.edu/avi/os-book/OS9/slide-dir/
- “Data Systems for Machine Learning” (lectures) by Arun Kumar (ISBN: 3031007417)https://cseweb.ucsd.edu/classes/wi24/cse234-a/schedule.html and “Data Management in Machine Learning Systems” (book), by Matthias Boehm, Arun Kumar, and Jun Yang (ISBN: 1681734966)

Assessment

Assessment Type	Grade Percentage
Research Paper Summaries
	%
Class Participation	0%
Research Paper Presentation	%
Final Exam	%

Research Paper Summaries: Students must submit a summary of the research article discussed before each class. Summaries are graded on a 1-5 scale based on adherence to the provided guidelines.
Quizzes on Research Articles: At the start of three predetermined lectures, students complete a short 30-45 minute quiz consisting of 5–6 questions on the assigned article. Students are allowed to use only the printed article, their own summary of it, and up to 5 pages of additional notes they have prepared. The notes must not be in the form of AI-generated questions and answers. No electronic devices are permitted. Answers are graded on a scale of 1 to 5.
Class participation: In each session, students who are present receive 0.25 out of 100 points for attendance and an additional 0.25 points for active participation through questions, answers, and critical discussion. For example, assuming there are 20 lectures (the first half of the semester has only one 3-hour lecture per week), a student who is present in all sessions but does not participate at all receives 20 × 0.25 = 5%.
Research Paper Presentations: Groups of two students prepare a 60-minute presentation on the discussed paper. Presentations should be thorough (30-40 slides) and encourage questions and discussion. Undergraduate students must present in group of four.
Final Exam: It includes questions based on the research articles and lecture notes covered in the course material. The exam is open notes and open articles, but students will not have access to the internet or electronic devices. Students are expected to bring the printed research articles, designated course notes or slides, the paper summaries and 5-pages notes per paper (not in Q&A form) they prepared. A prerequisite for passing the course is achieving a score that is at least 40% of the final exam maximum grade (20/50).
Graduate students who have already taken the undergraduate version in a previous year must present two research articles in groups of two. Each presentation accounts for 20% of the total grade. Additionally, they must submit and present (using 10 slides) a semester project involving the use and/or modification, and/or evaluation of a scalable computational framework covered in the course. The semester project constitutes 60% of the total grade.

The instructor reserves the right to make minor changes to the grading criteria and to adjust grades upwards to ensure fairness.

Language

English

CSE 463 Advanced Topics in Software Engineering

Course Title

ADVANCED TOPICS IN SOFTWARE ENGINEERING

Course Code

CSE 463

Course Type

Compulsory

Level

BSc

Year / Semester

Fourth year/ Fall semester

Instructor’s Name

Andreas Andreou

ECTS

Lecture / week

Workshops / week

Course Purpose and Objectives

This course delves into specific areas and advanced topics of Software Engineering with emphasis on three axes:

Software Engineering for Distributed Systems and the Cloud
Process Mining
Software Reuse

Learning Outcomes

Summarize the key principles, problems, and challenges in software development for distributed systems and cloud computing
Evaluate the DevOps approach and its benefits in software engineering
Develop and use service-oriented software systems (online and offline)
Explain distributed transaction processing and blockchain-based software systems
Understand and apply main concepts and principles of process mining using real-world examples
Identify and apply methods of software development through reuse, particularly scalable systems with software components

Prerequisites

Subjects in the field of Software Engineering

Required

Course Content

The course syllabus includes the following.

Part A’ – Software Engineering for Distributed Systems and the Cloud

Chapter 1 - Distributed Software Engineering
Chapter 2 – Introduction to DevOps
Chapter 3 - Software Engineering and Cloud Computing
Chapter 4 - Service-oriented Software Engineering
Chapter 5 - Introduction to Blockchain

Part Β’ – Process Mining

Chapter 6: Introduction to Process Mining
Chapter 7: Process Modelling and Analysis
Chapter 8: Getting the Data

Part C’ – Software Reusability

Chapter 9 - Software Reuse
Chapter 10 - Component Based Software Engineering

Teaching Methodology

Weekly lectures using related material slides that are prepared and made available by the instructor. Research articles for study and presentation to the class audience.

Bibliography

Basic:

“Software Engineering”, Ian Sommerville, Addison-Wesley, 9th ed., 2011 (or more recent edition). (ISBN 0137035152)

Complementary:

“Software Engineering”, 4th ed., Pfleeger, S. L. and Atlee, J. M., Pearson, 2010. (ISBN: 0136061699)
“Software Engineering, A Practitioner’s Approach, European Adaptation”, Roger S. Pressman, 5th Edition, McGraw-Hill, 2000 (ISBN: 9780077096779) (or more recent edition).
“Software Engineering: Principles and Practice”, 3rd ed., H. van Vliet, John Wiley & Sons, 2008. (ISBN: 0470031468)

Research Articles.

Assessment

Assessment Type	Grade Percentage
Study and presentation of Article	15%
Project(s)	40%
Final exams*	45%

* provided that the project(s) is(are) successfully concluded

Language

Greek

CSE 466 Advanced Computer Architecture

Purpose and Objectives: The course explores the design and analysis of modern computer architecture, with an emphasis on high-performance and energy-efficient systems. Building fundamental computer architecture, students will delve into advanced topics such as multicore processing. The course also covers cutting-edge industry trends and research directions, including AI accelerators, GPU and TPU design, RISC-V, domain-specific architectures, and hardware security. Through a combination of theory, simulation tools, case studies, and discussions of scientific and research articles, students will gain a deep conceptual understanding and practical skills in evaluating and designing complex architectures that power modern computing systems.

Learning Outcomes: Upon completion of the course, students will be able to: 1) Describe and explain advanced principles governing the architecture of modern types of processors and optimizations, as well as their impact on application performance. 2) Evaluate current technologies in the field of computer architecture. 3) Understand, assess, and analyze scientific articles in the field of Computer Architecture. 4) Conduct research at an advanced undergraduate level through the development of fundamental research skills.

Course Content: Basic processor design principles, Parallelism and parallel architectures, Scalable Multi-core and Many-core Systems, Interconnection Networks and NoC (Network-on-Chip), Emerging and High-performance computing, GPU Architecture and GPGPU Programming, Specialized AI Accelerators, RISC-V Architecture and Open-ISA Revolution, Heterogeneous Computing, Accelerators and Domain-specific architecture, FPGAs in the Datacenter, Neuromorphic Computing, Quantum-Aware Architectures, In-Memory Computing, Security in architecture, Side-channel Attacks and Defenses, Trusted Execution Environments (Intel SGX, AMD SEV), Current research directions and trends, Open Hardware Movement and Hardware-Software Co-design, Approximate Computing.

CSE 467 Scalable Data Processing Systems

CSE 469 Mobile Computing and Applications

Course title	Mobile Computing and Applications
Course code	CSE 469
Course type	Mandatory
Level	Undergraduate
Year / Semester	4th year/8th semester
Teacher’s name	Special Scientist
ECTS	5	Lectures / week	2x2h		Laboratories / week		-
Course purpose and objectives	This course introduces students to the foundational principles and emerging practices of mobile computing and application development. Through a blend of theoretical exploration and hands-on labs, students will gain a solid understanding of the mobile software stack, wireless and ad-hoc networking, location-aware services, mobile data management and context-aware systems. Emphasis is placed on the design and development of modern mobile applications that interact with backend cloud services, utilize on-device sensors, and leverage technologies such as Bluetooth, Wi-Fi, RFID, NFC, and cellular networks. The course also explores cutting-edge topics including wearable computing, mobile sensing, and crowdsensing.
Learning outcomes	Design and develop mobile applications using modern tools, frameworks, and platforms Analyze and apply wireless, ad‑hoc, and mobile networking principles in application design Implement localization, context‑aware, and sensor‑based functionalities in mobile systems Integrate mobile apps with cloud back‑end services and mobile data management approaches Evaluate and apply emerging mobile computing technologies such as RFID, NFC, BLE, and IoT
Prerequisites	CSE 224 – Data Structures and Algorithms, CSE 321 Computer Networks and Communications			Required		-
Course content	Outline: Basics of Mobile Apps Mobile App Development Tools & Strategies Localization Mobile Cloud and Back-End Servers Location Awareness Context-Aware Systems Sensors and Sensing RFID & NFC Fundamentals of Networks Wireless Networks Wi-Fi Ad-Hoc Networks Bluetooth and BLE Cellular Networks Wearable Computing & Internet of Things Mobile Sensing 17. Crowdsensing
Teaching methodology	Presentations are conducted by the instructor using slides. The project lasts the entire semester. During the lectures, an in-depth discussion with the instructor is often held with questions and answers. Small homework assignments are given in class to the students to help them better understand the concepts being taught. Students are given enough time to work on possible solutions to these exercises and discuss them with the instructor during the lecture and at the end, an indicative solution is presented and explained to them.
Bibliography	There is no required textbook, as this field evolves at a very fast pace. Instead, scientific articles will be provided throughout the course, rather than relying on one textbook. Supplementary Textbooks: • Ubiquitous Computing Fundamentals, 1st Edition, edited by John Krumm CRC Press, 2009. ISBN: 978-1-4200-9360-5 • Spatial Gems, Volume 1, by John Krumm; Edited by Andreas Zufle and Cyrus Shahabi ¨ ACM Books, 2022. ISBN: 978-1-4503-9811-4
Assessment	Homework Assignments 20% Term Project 25% Midterm Exam 20% Final Exam 30% Class Attendance & Participation 5%
Language	Greek

CSE 470 Integrated Circuits Design and Testing

Course title	Integrated Circuits Design and Testing
Course code	CSE 470
Course type	Elective
Level	Undergraduate
Year / Semester	4th (Fall)
Teacher’s name	Harris Michail
ECTS	5	Lectures / week	2 *2 hours		Laboratories / week
Course purpose and objectives	The course aims to emphasize in specific areas and advanced topics of Integrated Circuit Design and Testing such as: • The design flow and implementation of hardware circuits and systems as well as the analysis and comparative evaluation of the various alternative circuit design architectures. • Optimization techniques and their practical application for digital circuits and systems hardware design • Testing and testability of ICs, the valuation of ICs production lines (yield), the economics of ICs testing etc. • Fault Models and faults detection as well design for testability (DFT) in ICs.
Learning outcomes	The Learning outcomes of this course are: Evaluation and application of the various alternatives of digital systems hardware designs, architectures, design strategies and implementations for designing high speed, low-power and low-integration area digital circuits and systems Comparative evaluation and application of optimization techniques at all levels of the design flow Acquiring extensive experience in designing, optimizing and implementing hardware digital circuits and systems using VHDL through weekly mini lab exercises homework. Analysis and evaluation of various integrated circuits testing techniques, taking in consideration economic aspects of ICs design and implementations. Use and practical application of techniques for fault modeling and detection at logic level in ICs and techniques for design for testability (DFT).
Prerequisites	CSE 122 and CSE 323			Required
Course content	Basic and advanced techniques for developing, designing and testing integrated circuits. Digital design optimization techniques in terms of various designs parameters (speed, power, area) as well as practical approaches in testing the designed Integrated Circuits. Analysis of digital hardware design flow, design levels and implementations, Top/Down and Bottom/Up hardware design approach with examples. Timing and optimizations, Pipelined Digital Design for high throughput, Parallel Digital Design for low area consumption, Parallel, Algorithmic Loop Unfolded and Pipelined Digital Designs for low area consumption, Testbences for hardware designs simulations, Challenges in 90nm Digital Hardware Design. Digital Hardware Design with hardware description language VHDL. Extensive case study, analysis and optimized redesign of hardware designed cryptographic algorithm: Pipelined SHA-1 Hash function. Introduction to the need for testing, Failures, Defects and Fault Models, Stuck-at Faults, Simple and multiple stuck-at faults, Faults coverage, Bridging Faults, Logic fault simulation, Serial, parallel, deductive and concurrent fault simulators, Test patterns generation, Algebraic and algorithmic methods for test patterns generation, Design for testability, Observation and control points, Pseudoexhaustive testing, Scan path design technique.
Teaching methodology	Presentations, Problems Solving, Group Discussions, Laboratory demonstrations/exercises and Homework Laboratory exercises
Bibliography	Harris Michail, "Educational Material and Laboratory Exercises" K. C. Chang, “Digital Systems Design with VHDL and Synthesis: An Integrated Approach”, IEEE Computer Society, 1999 S. Brown, Z. Vranesic, “Digital Logic with VHDL Design”, Translated in Greek, 3rd edition Harris Michail, "Tutorials for High Level synthesis tools Parts Α and Β", Cyprus University of Technology (C.U.T) S. Mourad and Y. Zorian, “Principles of Testing Electronic Systems” Wiley and Sons, New York, July 2000
Assessment	Midterm exam (30%), Homework (mini-tasks) (20%), Final examination (50%)
Language	Greek

CSE 471 Deep Learning I

Course Title	Deep Learning I
Course Code	CSE 471
Course Type	Elective
Level	BSc
Year / Semester	4th year/1st semester
Teacher’s Name	Dr. Sotirios Chatzis
ECTS	5	Lectures / week		3h	Laboratories / week		-
Course Purpose and Objectives	Deep Learning 1 is the first part of a two-course sequence on deep learning, focusing on core concepts and techniques. This updated module emphasizes practical design patterns that underpin modern deep networks: normalization layers, residual connections, and robustness to adversarial perturbations. It also introduces probabilistic generative modeling via Variational Autoencoders (VAEs). Students will learn how to build and train neural networks effectively, understand why normalization and residual links stabilize training, implement a simple VAE, and reason about/adapt models under adversarial threat models. By the end of the course, students will be prepared with a solid base for more advanced topics in Deep Learning 2 and for leading successful machine learning projects.
Learning Outcomes	By the end of the course, students will be able to: • Describe and classify the fundamental design principles of deep learning systems and neural network architectures. • Implement and use widely accepted techniques for constructing and training deep neural networks (including parameter tuning and basic regularization). • Explain and implement normalization layers (BatchNorm, LayerNorm, GroupNorm) and understand their effect on optimization dynamics. • Build and train residual architectures (e.g., ResNet-style models) and justify the role of skip connections in deep networks. • Implement a basic Variational Autoencoder (VAE) with the reparameterization trick, and evaluate learned latent representations. • Craft simple adversarial attacks (e.g., FGSM/PGD), perform basic adversarial training, and evaluate model robustness. • Summarize and critically evaluate research articles on core deep learning topics. • Apply deep learning methods to carry out a complete project, from problem formulation to model implementation and evaluation, on a practical task.
Prerequisites	Undergraduate-level knowledge in linear algebra and statistics (basic familiarity with calculus and probability is also expected).		Required			-
Course Content	Introduction to Deep Learning: Overview of deep learning history, significance, and typical use cases. Neural Network Basics: Perceptrons, activation functions, multilayer feed-forward networks, and the backpropagation algorithm. Fundamentals of Representation Learning: The principle of learning data representations (features) automatically. Training Deep Neural Networks: Optimization basics for deep learning – stochastic gradient descent (SGD) and its variants; loss functions; practical considerations for training stability. Regularization Techniques: Strategies to prevent overfitting in neural networks (e.g., L2 regularization, dropout, early stopping) and their impact on model generalization. Normalization Layers and Training Dynamics: Batch Normalization, Layer Normalization, and Group Normalization; why and when they help; initialization and batch-size considerations. Convolutional Neural Networks (CNNs): Principles of convolutional layers and pooling, classic CNN architectures for image recognition, and understanding how CNNs extract spatial features. Residual Connections & Deep Architectures: Skip connections, identity mappings, pre-activation ResNets; training very deep networks in practice. Variational Autoencoders (VAEs): Latent-variable models, ELBO and the reparameterization trick, β-VAEs and disentanglement; simple applications in image generation. Adversarial Robustness: Threat models; gradient-based attacks (FGSM, PGD); robustness evaluation; basics of adversarial training and regularization-based defenses.
Teaching Methodology	Teaching is conducted through weekly lectures and interactive discussions. During the first seven weeks, the instructor delivers lectures covering the foundational topics of deep learning as outlined above. In the remaining six weeks of the semester, students actively participate by presenting selected research papers related to these topics. Throughout the course, the instructor provides lecture slides and notes and engages students with oral questions about the material and the assigned articles. Students are expected to prepare brief summaries of the research articles assigned to them and to contribute to discussions. This blend of lectures, student presentations, Q&A sessions, and written summaries is designed to promote reflection, deepen understanding, and cultivate critical thinking skills in applying deep learning methods.
Bibliography	Primary Materials: Research articles (accessible through the course Moodle page) serve as the main study materials, illustrating both classic and current developments in deep learning. Textbook: Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016. (ISBN: 0262035618) (This seminal textbook is used as a supplementary resource for fundamental concepts and theories of deep learning.) Additional resources: Instructor-provided notes and relevant online resources or textbook chapters will be recommended for specific topics (e.g., documentation and tutorials for deep learning frameworks, when applicable). Selected seminal papers for the added topics: He et al., 2016 (Deep Residual Learning); Ioffe & Szegedy, 2015 (Batch Normalization); Ba et al., 2016 (Layer Normalization); Wu & He, 2018 (Group Normalization); Kingma & Welling, 2014 (Variational Autoencoders); Goodfellow et al., 2015 (Adversarial Examples).
Assessment	Presentations of Research Articles (50%) Final exam (50%)
Language	English

CSE 472 Deep Learning II

Course Title	Deep Learning II
Course Code	CSE 472
Course Type	Compulsory
Level	BSc
Year / Semester	4th year/2nd semester
Teacher’s Name	Dr. Sotirios Chatzis
ECTS	5	Lectures / week		3h	Laboratories / week		-
Course Purpose and Objectives	Practical systems view with Hugging Face: Principle of attention; intuition for keys/queries/values Hugging Face ecosystem (Transformers, Diffusers, Datasets, Accelerate) Multimodal modeling; zero-shot & few-shot learning; in-context inference Inference algorithms (greedy, beam, top-k/top-p, temperature; CFG for diffusion) Quantization and efficient inference
Learning Outcomes	Upon successful completion, students will be able to: Explain self- and cross-attention and their roles in modern architectures Use Hugging Face Transformers and Diffusers for NLP, vision, and multimodal tasks Apply zero-/few-shot learning, prompt design, and in-context inference Run and compare inference algorithms; manage batching/caching/streaming Quantize models (8-bit/4-bit) and analyze efficiency–quality trade-offs Fine-tune with LoRA/QLoRA and evaluate effectiveness Implement and analyze diffusion pipelines, schedulers, and guidance Critically read and present recent research on transformers and diffusion
Prerequisites	-		Required			YES
Course Content	Practical systems view with Hugging Face: Attention in practice; why it enables transfer and compositionality HF overview: model hub, pipelines, datasets; pre-trained transformers and diffusion models Multimodal modeling (e.g., text–image); CLIP-style encoders; basic VLM pipelines Zero-/few-shot learning; prompt engineering; in-context examples and evaluation Inference for LMs: greedy, beam, top-k/top-p, temperature; batching, KV caching, streaming Inference for diffusion: schedulers (DDIM, DPM-Solver, Euler), classifier-free guidance, negative prompts Quantization & efficiency: 8-bit/4-bit (bitsandbytes), weight-only vs activation-aware; throughput vs fidelity Mini-labs: zero-shot classification; few-shot prompting; 4-bit LM inference; text-to-image with scheduler swaps; serving a small model
Teaching Methodology	Lectures integrated with live, hands-on demos (Part I) using the Hugging Face stack, followed by paper-centric deep dives (Part II). Students work with notebooks demonstrating Transformers and Diffusers, including parameter-efficient fine-tuning and quantized inference. Weekly reading summaries and in-class Q&A support both practical proficiency and theoretical depth.
Bibliography	Primary materials: curated recent research articles. Key references: Vaswani et al., “Attention Is All You Need,” 2017 Ho et al., “Denoising Diffusion Probabilistic Models,” 2020; Dhariwal & Nichol, 2021 Hu et al., “LoRA: Low-Rank Adaptation,” 2021 Dettmers et al., “QLoRA,” 2023 Hugging Face Transformers & Diffusers documentation/tutorials
Assessment	Presentations of Research Articles (50%) Final exam (50%)
Language	English

CSE 473 Introduction to Data Science

Course title	Introduction to Data Science
Course code	CSE473
Course type	Mandatory
Level	Undergraduate
Year / Semester	4th year / 2nd semester (Spring)
Teacher’s name	Simos Gerasimou
ECTS	5	Lectures / week	4		Laboratories / week
Course purpose and objectives	This course focuses on the key concepts and practices involved in the rigorous execution of a data science project. Students will be introduced to the data science process and lifecycles typically undertaken for the realisation of a data science project. Students will also become familiar with the processes for collecting, manipulating and cleaning data, while gaining experience in assessing the quality of data. Relational databases, SQL, and other database paradigms, such as NoSQL, are covered as a way of storing and accessing data. Students will be introduced to data distributions and statistical analysis in data science, including correlation, confidence intervals and inferential statistics, and how to use them in a programming environment. Finally, students will gain experience with different machine learning problem domains (e.g., supervised, unsupervised, and reinforcement learning) and develop an appreciation of machine learning models available, in addition to ‘hands-on’ experience for designing, developing, and deploying appropriate data pipelines that integrate these models. Best approaches and ethics in data science will also be addressed. On completing this course, students will have developed a strong foundation of applications-focused knowledge and practical skills stretching across the entire data science pipeline; they will be able to design, develop, deploy, and evaluate their own data science solutions.
Learning outcomes	By the end of the course, students will be able to: Distinguish between different types of data that are generated in science, engineering and design, and employ strategies for ensuring data quality. Retrieve data from a variety of different data sources in a variety of different formats. Apply inferential statistics and statistical procedures to test hypotheses about features and relationships within data sets. Use appropriate visualisations to present and explore data sets. Use databases, both relational and of other paradigms, to store and query data. Implement supervised and unsupervised machine learning algorithms. Identify the ethical concerns regarding the provenance of data, the privacy of individuals, and the impact data analytics can have on society, and apply topics from the code of ethics of a professional data protection body.
Prerequisites	Undergraduate-level knowledge in Python programming (basic familiarity with statistics and probability is also expected).			Required
Course content	Week 1: Introduction to data science, data science lifecycles and paradigms Week 2: Data collection, exploratory data analysis, data visualisation Week 3: Structured and unstructured data, relational databases Week 4: Database normalisation, Structured Query Lanrguage, Database APIs, NoSQL Week 5: Data processing and data integrity Week 6: Probabilities, central limit theorem, confidence intervals, covariance, correlation Week 7: Inferential statistics (parametrics and non-parametric) Week 8: Machine Learning introduction and supervised learning (regression) Week 9: Supervised learning (classification) Week 10: Unsupervised learning (clustering, dimensionality reduction) Week 11: Data science ethics, standards/laws/guidelines on data Week 12: Data science models deployment and evolution Week 13: Project presentations
Teaching methodology	Teaching is carried out via weekly lectures, using with slides, notes and references, distributed by the lecturer. The lectures will be complemented by interactive discussions that encourage reflection and critical thought and a practical component that helps students to become familiar with and develop a deeper understanding of the subject matter.
Bibliography	Spiegelhalter, D., The Art of Statistics: Learning from Data, Pelican, 2019. (ISBN: 0241398630) VanderPlas, J. Python Data Science Handbook: Essential Tools for Working with Data, O’Reilly, 2016. (ISBN: 1491912057) Igual, L. Segui, S. Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications, Springer, 2017 (ISBN: 978-3031489563)
Assessment	Project Report and Presentations (50%) Final exam (50%)
Language	Greek

CSE 474 Introduction to Finance & Management Principles for IT Companies

Course title	Introduction to Finance & Management Principles for IT Companies
Course code	CSE 474
Course type	Elective
Level	Undergraduate
Year / Semester	4th year / 1st semester
Teacher’s name	Special Scientist
ECTS	5	Lectures / week	2x2h		Laboratories / week		-
Course purpose and objectives	The purpose of this course is to equip 4th year Computer Science and Engineering students with a foundational understanding of how technological businesses operate beyond the technical domain. While CSE graduates often excel in coding and systems design, they also need literacy in finance and management to effectively contribute to — and eventually lead — tech organizations. This course bridges the gap between technical expertise and business and finance decision-making, preparing students for highly qualified careers in the tech industry. Course Objectives: Interpret financial information relevant to IT companies, including financial statements, cost structures, and performance metrics. Differentiate between business models (subscription, SaaS, ad-supported, marketplace, hardware, consulting) and evaluate their implications on revenue and growth. Analyze cost structures and valuation methods for software, hardware, and IT services companies, including startup funding and scaling challenges. Apply management principles such as agile methodologies, leadership styles, and team scaling strategies to technology-driven organizations. Develop and present business cases by integrating financial, managerial, and technical perspectives, both for existing IT companies and hypothetical startups. Assess investment and funding strategies in the tech sector (venture capital, IPOs, angel investment) and their relevance to innovation and entrepreneurship. Connect finance and management concepts to career development as technical professionals who contribute to product strategy, business sustainability, and innovation.
Learning outcomes	By the end of the course, students will be able to: Understand the basic financial statements and how IT companies measure performance Recognize the cost structures and revenue models typical of software, hardware, and IT services companies Explain management principles relevant to tech organizations (agile, product management, scaling teams) Evaluate investment, funding, and valuation of startups Apply basic budgeting and project finance concepts to IT projects
Prerequisites	-			Required		-
Course content	Week 1: Introduction – Why CS grads need finance & management literacy; IT industry overview. Week 2: Finance Fundamentals – Revenue, cost, profit, cash flow; core financial statements; interpreting IT company financials. Week 3: Business Models in IT – Subscription, freemium, ad-supported, marketplace; case studies (Google, Netflix, Atlassian). Week 4: Cost Structures & Valuation – Fixed vs variable costs; R&D as investment; startup valuation methods. Week 5: Management Principles – Leadership in IT; agile vs traditional management; scaling teams. Week 6: Project Finance – Budgeting IT projects; ROI; build vs buy; mobile app budgeting case. Week 7: Funding & Investment – Angel, VC, IPOs; risk-return in startups; pitch deck essentials. Weeks 8–12: Student Workshops & Presentations – Group cases on IT companies/startups (business model, finances, management, risks). Week 13: Recap & Career Perspective – Linking finance & management to CS careers; reflection & Q&A.
Teaching methodology	Teaching is carried out via weekly lectures, using slides, notes and references, distributed by the lecturer. The lectures will be complemented by interactive discussions that encourage reflection and critical thought and a practical component that helps students to become familiar with and develop a deeper understanding of the subject matter.
Bibliography	Corporate Finance, Global Edition 5th Edition by Jonathan Berk and Peter DeMarzo (ISBN: 1292304154) The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses, by Eric Ries (ISBN: 9780307887894) High Output Management, by Andrew S. Grove (ISBN: 9780679762881)
Assessment	Group project & presentation (50%) Final exam (50%)
Language	Greek

EEN 214 Electronics I

Course title

Electronics I

Course code

EEN 214

Course type

Mandatory

Level

Undergraduate

Year / Semester

2nd year/4th semester

Teacher’s name

Efthyvoulos Kyriacou

ECTS

Lectures / week

Laboratories / week

Course purpose and objectives

Understand the function and characteristics of semiconductors for specific applications. The student should fully analyze the basic circuits with diodes and transistors

Learning outcomes

Acquire comprehensive knowledge of the analysis and operation of circuits with diodes and transistors
Design and implement circuits with diodes and transistors

Prerequisites

Circuit Theory

Required

Course content

Semiconductor diodes
Bipolar junction transistors
Field effect transistors
Bias and stability
Small signal amplifiers
Differential Amplifiers
7) Frequency response

Teaching methodology

Lectures, home work, midterm, final exam. Practical problem solving during lectures

Bibliography

"Electronic Circuit Analysis and Design", D. Neamen Lecture notes
“Microelectronic Circuits” Adel Sedra, Keneth C. Smith, Oxford University Press, 7th Edition, 2015. ISBN: 978–0–19–933913–6.
“Electronic Devices and Circuit Theory”. Robert Boylestad, Louis Nashelsky, Prentice Hall. 11th edition (ISBN: 0132622262)

Assessment

Mid term exam Ι	30%
HW	10%
Final Exam	60%

Language

Greek

EEN 215 Electrical Laboratory II

Course Title

Electrical Laboratory II

Course Code

EEN 215

Course Type

Mandatory

Level

BA Degree

Year / Semester

2nd year/ 4th semester

Teacher’s Name

Harris Michail

ECTS

Lectures / week

1 * 1 hour

Laboratories / week

1* 3 hours

Course Purpose and Objectives

The objectives of this course are:

Students to gain hands-on experience in digital circuit design and in usage of integrated ICs both in educational boards and with CAD tools in FPGAs.
Students to acquire practical knowledge and practice the necessary design approaches for designing digital systems such as reusability, hierarchical design, abstraction levels, etc.
Students to acquire practical knowledge and practice simulation techniques and debugging during the design phase.
The study and understanding of the basic functional principles of digital systems and circuits, their parameters, their design constraints and the technologies for their implementation and design.
The use and understanding of the structure and utilization of FPGA devices, the schematic description of combinational and sequential logic circuits, as well as the comparison to the description of digital circuits and systems with hardware languages (HDLs).
Implementation in educational boards as well as with CAD tools in FPGAs, while understanding the basic operation principles, of combinational and sequential logic design, as well as their optimal design and the various implementation parameters.
Analysis, design and implementation of counters (synchronous and asynchronous) as well as of more complex designs like ALUs both in educational boards and with CAD tools in FPGAs.

Learning Outcomes

At the end of this course the students will have:

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Prerequisites

EEN 212

Required

Course Content

Introductory course to lab equipment, familiarization with basic logic gates and design and implementation of combinational circuits with SSI and MSI ICs.
Design and implementation of Multiplexers, Encoders, Decoders, Demultiplexers, Comparators, Adders etc in educational board.
Familiarization with Flip Flops: S-R flıp flop, D flıp flop, J-K flıp flop. Preset and Clear functıons. Design and implementation of simple digital sequential circuits with SSI and MSI ICs in educational breadboards.
Design and Implementation of Synchronous and Asynchronous Counters (educational breadboards).
Introductory course to: design software ALTERA Quartus II, to programmable logic devices and implementations of digital circuits on FPGAs, FPGAs vs ICs, Schematic Description vs HDL Description, Simulation and Debugging in Altera Quartus II, FPGA Downloading.
FPGA implementation of combinational circuits: Adders, Comparators, Multiplexers, Encoders/Decoders, Hierarchical and Cascade Design. Re-use technique
Design and FPGA implementation of sequential circuits: S-R flıp flop, D-Type flıp flop, J-K flıp flop.
Design and Implementation of Synchronous and Asynchronous Counters in FPGAs.
Hierarchical design and FPGA implementation of Arithmetic and Logic Unit (ALU). Advantages and disadvantages of levels of abstraction, Schematic Description vs HDL Description

Teaching Methodology

Presentations, Laboratory demonstrations/exercises and Group Discussions

Bibliography

Harris Michail, Educational Material and Laboratory Exercises
"Digital Design", Mano Morris, Ciletti Michael, 5th edition. (ISBN: 0132774208)
"Digital Design : Principles & Practices", John F. Wakerly, 4th edition. (ISBN: 978-9332535657)

Assessment

Midterm Lab exam (30%), Lab reports (20%), Final Lab examination (50 %)

Language

Greek

EEN 318 Digital Signal Processing

Purpose and Objectives: Students should assimilate the necessary theoretical and practical knowledge about the various phases in digital processing of one-dimensional and two-dimensional digital signals. Design and analysis of digital systems and their applications / implementations using programming. The course is offered through a combination of lectures, exercises and programming workshops.

Learning Outcomes: Gain comprehensive knowledge of signal analysis, specifications, operation, design, analysis and programming of digital systems. Also obtain knowledge of discrete-time systems and properties, continuous and discrete Fourier transform and applications. Introduction to the design of finite and infinity digital filters (FIR and IIR) impulse response.

Course Content: The course is based on the basic elements of the analysis and processing of digital signals and systems, offered in the prerequisite course, and has as its main objectives the following: Recapitulation on signals, systems, sinusoidal sampling. Aliansing and folding. Sampling theorem. Discrete-time systems and properties. Impulse response. Convergence. Application in MATLAB environment. Series, Fourier transform and properties. System Frequency Response. Finite and Infinite Impact Response Systems (FIR, IIR). Applications in MATLAB environment. Frequency response of FIR and IIR systems. Linear phase. Frequency sampling. Discrete Fourier transform. Applications in MATLAB environment. Fourier discrete transform properties and applications. Signal spectrum analysis. Circular convolution. Introduction to digital filters. Design of FIR filters using windows. Applications in MATLAB environment. Design of FIR filters by frequency sampling. Z-Transform. Implementation in MATLAB environment. Design of IIR filters. Implementation of discrete time systems. The following chapters are covered: 1. Sampling of sinusoids signals. Sampling theorem. 2. Discrete-time systems and their properties. 3. Fourier Series and Fourier Transform and Properties. Finite and Infinite convolution (Response) Systems (FIR, IIR). 4. Frequency sampling. Discrete Fourier transform. Circular convolution. 5. Introduction to digital filters. Design of FIR filters using windows and frequency sampling. 6. Transformation Z. 7. Design of IIR filters.

EEN 413 Introduction to Photonics

Course title

Photonics

Course code

EEN 413

Course type

Elective

Level

Undergraduate

Year / Semester

4rd year/8th semester

Teacher’s name

Kyriacos Kalli

ECTS

Lectures / week

Laboratories / week

Course purpose and objectives

Understanding the basic properties of light, optical waveguides, and more generally the basic concepts of photonics.

Learning outcomes

Acquire fundamental knowledge of photonics concepts, light properties, and their applications
Explain the principles of light propagation, reflection, refraction, diffraction, and polarization
Understand the operation of optical sources such as LEDs and lasers, and their role in photonic systems
Analyze light propagation in optical waveguides and fibres, including dispersion and attenuation
Describe the function of optical detectors, photodiodes, and receiver systems in communication links
Understand modulation principles and techniques for transmitting information using light
Evaluate the role of noise, multiplexing, and optical devices in light-wave communication systems
Apply photonic concepts to the design of optical communication systems and assess system performance

Prerequisites

EEN 316 - Electromagnetism or teacher approval.

Required

Course content

Introduction to Photonics and Optical Communication

Overview of photonics and the applications in different areas such as information technology and communications, healthcare, life sciences, optical sensing, lighting, energy and manufacturing. The evolution of light-wave systems will be discussed before the basics of optical communications systems will be introduced.

Nature of light and the production of EM radiation for photonics applications

i) Wave Nature of Light

Light waves in homogeneous media. Refractive index. Group velocity and group index. Magnetic field, Irradiance and Poynting vector. Snell's law and total internal reflection. Fresnel's equations. Multiple interference and optical resonators. Temporal and spatial coherence. Diffraction principles. Polarisation. Methods to define the characteristics of light mathematically (Stokes parameters, Jones vectors & matrices) and how to determine these characteristics.

ii) Optical sources and lasers

Semiconductor science and materials. Semiconductor concepts and energy bands. Direct and indirect band-gap semiconductors: E-k diagrams. P-n junction principles and band diagram. Principles of light emission and amplification in lasers, lasers (pulsed and CW), light emitting diodes (LEDs), semiconductor lasers (edge emitting lasers and VCSELs). Steady-state rate equation. Single frequency solid state lasers. Optical amplifiers.

Optical waveguides

The propagation of light in optical waveguides.

Dielectric waveguides and optical fibres. Symmetric planar dielectric slab waveguide. Modal and waveguide dispersion in the planar waveguide. Step index fibre. Numerical aperture. Dispersion in single mode fibres. Bit-rate, dispersion and optical non-linearities, Electrical and optical bandwidth. Graded index optical fibre. Attenuation in optical fibres-light absorption and scattering. Fibre manufacture.

Optical detectors and receivers

The detection of light and the demodulation of light including photoconductors, photodiodes and receiver systems.

Principle of the p-n junction photodiode. External photocurrent. Absorption coefficient and photodiode materials. Quantum efficiency and responsivity. The p-i-n, avalanche and heterojunction photodiodes. Phototransistors. Photoconductive detectors and photoconductive gain. Noise in photodetectors. Generic system issues: sources of noise and signal-to-noise ratio, limitations on temporal response and effective bandwidth.

Imparting information onto EM radiation & communication techniques

i) Basic modulation principles

Polarisation and modulation of light. Light propagation in anisotropic media: birefringence. Birefringent optical devices. Optical activity and circular birefringence. Electro-optic effects. Integrated optical modulators. Acousto-optic modulator. Magneto-optic effects. Non-linear optics and second harmonic generation.

ii) Modulation

Acousto-optic and electro-optic techniques, LED switching, analogue and digital techniques using lasers, AM, FM, phase modulation techniques

Applications for light-wave systems

A summary of important concepts of digital communication including base band and broadband digital transmission, bit error rate, bit group error rate and time division multiplexing (TDM) and wavelength division multiplexing (WDM). Trends and new directions in photonic applications

i) Noise and detection

Noise arising from the properties of fibres, transmitters, receivers and amplifiers as well as the determination of the bit error rate.

ii) Optical MUX and DEMUX

The operating principle of multiplexers and demultiplexers. Different optical devices, essential to optical networks, optical amplifiers, polarisation control devices, optical isolators, optical filters and diffraction gratings, modulators and switches.

iii) Optical systems design

Design process for a point-to-point optical links.

Teaching methodology

Presentations, group discussion and solving exercises.

Bibliography

Fundamentals of Photonics (Wiley Series in Pure and Applied Optics) by Bahaa E. A. Saleh and Malvin Carl Teich (Hardcover - Mar. 9, 2007) (ISBN: 0471358320)
Photonics: Optical Electronics in Modern Communications (The Oxford Series in Electrical and Computer Engineering) by Amnon Yariv and Pochi Yeh (Hardcover - Jan. 26, 2006) (ISBN: 0195179463)
Essentials of Photonics, Second Edition (Optical and Quantum Electronics) by A. J. Rogers (Paperback - Dec. 5, 2008) (ISBN: 0849338360)
Any other books English or Greek that you find useful on Photonics

Assessment

Final Examination
Midterm Examination
Class & Homework Assignments

Language

Greek

EEN 444 Digital Image Processing

Course title	Digital Image Processing
Course code	EEN 444
Course type	Elective
Level	Undergraduate
Year / Semester	4th year/7th semester
Teacher’s name	Christos Loizou
ECTS	5	Lectures / week	2x2h		Laboratories / week		-
Course purpose and objectives	Students will integrate in this course the necessary theoretical and practical knowledge around the various phases in designing and implementing high performance digital signal, image and video integration systems with design automation tools. This will be accomplished through a combination of lectures, exercises and workshops.
Learning outcomes	Acquire comprehensive knowledge of image processing, analysis, compression, transfer, and segmentation for 2D and 3D signals Design and analyze 2D digital image filters Implement image analysis and processing systems using Python and OpenCV environments Apply image compression standards such as JPEG and JPEG2000 Perform image correction, segmentation, and edge detection Apply digital video editing and analysis techniques Use algorithms and programming techniques to develop applications for image and video editing
Prerequisites	ΕΕΝ 318 – Digital Signal Processing			Required		-
Course content	The course is based on the basic elements of digital signal processing and analysis offered in the prerequisite course and has as its main objectives: Introduction to the basics of 1-dimensional and 2-dimensional digital signal theory and methods (image and video) use programming in Python/Open CV environments to implement and design integrated systems for image analysis. gain full-time experience in the process of editing and analyzing digital image analysis systems using a computer, through laboratory exercises Upon completion of the course, students will be expected to have a good knowledge of 2-D signal analysis and processing, the basic concepts and application areas of digital image processing, to understand the operation of basic multimedia image coding standards and to be able to use their programming experience for the development of image and video editing programs. Also be able to use image processing algorithms and appropriate techniques to solve specific problems. The following chapters are covered: Course introduction: Recovery, storage, transfer, image editing. Image illustration geometry Binary Image Processing Image Histogram and Point Functions Discrete Fourier Image Transformation Linear Image Filtering and Image Transformations, Nonlinear Picture Filtering Image Compression, JPEG, PACS: Image Encoding, Compression Templates (MP3, JPEG, JPEG2000 etc.) Image Analysis I: Image correction and segmentation, edge detection. Image Analysis II and Introduction to Digital Video Editing
Teaching methodology	Lectures. Every week, the course instructor will present an introduction to the topic in the form of a lecture presentation. The course will be a blend of presentations, problem solving, group discussions, examples use, Python programming, and laboratory exercises.
Bibliography	Representative readings R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd edition, Prentice Hall, 2007, ISBN-10: 013168728X. (http://www.imageprocessingplace.com/) Jan Erik Solem, Programming Computer Vision with Python, Creative Commons, 2012. (http://programmingcomputervision.com/downloads/ProgrammingComputerVision_CCdraft.pdf) additional reading is given below: R. C. Gonzalez, R. E. Woods and Edding, Digital Image Processing Using MATLAB, Prentice Hall, 2004, ISBN number ISBN number 0130085197 Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Digital Image Processing Using MATLAB, 2nd Ed., Gatesmark Publishing; 2nd ed. 2009, ISBN-10: 0982085400 Lecture Notes Research articles
Assessment	Lab exercises and projects (15%) Midterm Examination (35%) Final exam (50%)
Language	Greek

LCE 115 English for Computer Scientists and Engineers I

Course Title	English for Computer Scientists and Engineers I
Course Code	LCE 115
Course Type	Compulsory
Level	Undergraduate
Year / Semester	1st / 1st
Teacher’s Name	-
ECTS	4	Lectures / week		2 (2 hours)		Laboratories / week	-
Course Purpose and Objectives	Its general objective is to enable students to communicate competently at a B1-B2 level of the Common European Framework of Reference (CEFR) for Languages on issues related to students’ social and student life (university, curriculum, field of study), as well as on issues related to their future professional careers (occupations, short CV, etc.). Computer Assisted Language Learning (CALL) and culture awareness development are done through communicative situations deriving from authentic-like communication situations students may experience in their lives, during their studies and professional careers. Language competence is acquired through the use of text types, scenarios, and roles which promote the production and understanding of spoken and written language related to the topics covered. The course is based on post communicative era method, a student-centered, blended learning approach, and aims to familiarize students with authentic reading material related to general topics such as university student life, learning styles and language learning, academic topics such as how to study effectively, take notes and use the appropriate reference style and to specific topics related to the field of Electrical Engineering and Computer engineering and Informatics. The program builds on contemporary learning theories such as constructivism and social constructivism and student-centered teaching methods, and develops both student language skills and transferable 21st century skills such as communication, use of new information and communication technologies, collaboration, creativity and innovation, critical thinking and intercultural awareness as well as skills such as autonomous and lifelong learning. In particular, the course in Computer Engineering and Informatics aims to make students capable of managing English Bibliography, as well as understanding and editing material related to Computer Science and Informatics.
Learning Outcomes	By the end of the course, students shall be able to: Derive communicative circumstances, scenarios and roles through authentic situations of their social and student life (university, building access guidelines, their field of study), as well as topics related to their future career (workplace, occupations) industry, duties). Communicate with sufficient level B1-B2 proficiency of the Common European Framework of Reference (CEFR) for the languages (production and comprehension of oral and written language) on the above topics, through relevant and relevant textual types, scripts, and roles. Understand English-language written texts on Computer Engineering and Informatics, Engineering, various new technologies, and begin to develop the ability to critically analyze these texts. Have developed oral comprehension skills and adequate attendance of English language lectures and seminars by foreign visitors to the Department. Have developed sufficient individual, academic research skills to fulfill their respective obligations in the Electrical Engineering curriculum
Prerequisites	None		Required		None
Course Content	Computer Engineering and Informatics Degree - what does it mean? The Cyprus University of Technology - identity. Take notes. Types of learning Bibliographic references, paraphrases, plagiarism. Write a summary Library use skills. Dictionary skills (monolingual, bilingual) Career in Computer Engineering Familiarity with textiles from Computers Engineers and Informatics Digital knowledge The Information Age Graphical User Interfaces Multimedia Ducts The Internet Communication systems Robotics
Teaching Methodology	Interactive lectures of various types. Individual, group or collective comprehension and creation of verbal and written textual types of authentic type, specific communication circumstances. Active lifelong participation in interactive learning activities in a variety of forms. Active participation in interactive learning activities in a variety of forms, assisted by new information and communication technologies. Independent study.
Bibliography	Course books Eric H.Glendinning& John McEwan Oxford English for Information Technology Oxford University Press,2006 (ISBN: 0194573753) Evans,Virginia, Dooley Jenny and Wright Stanley, Career Paths: Information Technology, Express Publishing, 2011 (ISBN: 085777641X) Santiago Remacha Esteras. Infotech English for Computer Users student’s book (Cambridge Professional English) – Cambridge University Press, 2008 (ISBN: 052145980X) Santiago Remacha Esteras , Elena Marco Fabre Professional English in Use for Computers and the Internet,2007 (ISBN: 0521685435) Recommended supplementary materials: Printed dictionaries: Longman Dictionary of Contemporary English. Pearson Longman. Cambridge’s Advanced Learner’s Dictionary. Cambridge University Press. Concise Oxford English Dictionary. Oxford University Press. Online dictionaries: Cambridge dictionaries online: http://dictionary.cambridge.org/ The free online dictionary with sound: http://www.thefreedictionary.com/ Greek-English, English-Greek online dictionary: http://www.in.gr/dictionary/ Dictionary.com: http://dictionary.reference.com/ Merriam-Webster online dictionary: http://www.merriam-webster.com/home.htm English Pronouncing Dictionary with Instant Sound http://www.howjsay.com/
Assessment	Classroom exercises: 25% Homework and oral presentations:: 25% Midterm examination: 20% Final examination: 30%
Language	English

LCE 115 English for Computer Science and Engineering I

Purpose and Objectives: Its general objective is to enable students to communicate competently at a B1-B2 level of the Common European Framework of Reference (CEFR) for Languages on issues related to students’ social and student life (university, curriculum, field of study), as well as on issues related to their future professional careers (occupations, short CV, etc.). Computer Assisted Language Learning (CALL) and culture awareness development are done through communicative situations deriving from authentic-like communication situations students may experience in their lives, during their studies and professional careers. Language competence is acquired through the use of text types, scenarios, and roles which promote the production and understanding of spoken and written language related to the topics covered. The course is based on post communicative era method, a student-centered, blended learning approach, and aims to familiarize students with authentic reading material related to general topics such as university student life, learning styles and language learning, academic topics such as how to study effectively, take notes and use the appropriate reference style and to specific topics related to the field of Electrical Engineering and Computer Science and Engineering. The program builds on contemporary learning theories such as constructivism and social constructivism and student-centered teaching methods, and develops both student language skills and transferable 21st century skills such as communication, use of new information and communication technologies, collaboration, creativity and innovation, critical thinking and intercultural awareness as well as skills such as autonomous and lifelong learning. In particular, the course in Computer Science and Engineering aims to make students capable of managing English Bibliography, as well as understanding and editing material related to Computer Science and Engineering.

Learning Outcomes: By the end of the course, students shall be able to: Derive communicative circumstances, scenarios and roles through authentic situations of their social and student life (university, building access guidelines, their field of study), as well as topics related to their future career (workplace, occupations) industry, duties). Communicate with sufficient level B1-B2 proficiency of the Common European Framework of Reference (CEFR) for the languages (production and comprehension of oral and written language) on the above topics, through relevant and relevant textual types, scripts, and roles. Understand English-language written texts on Computer Science, Engineering, various new technologies, and begin to develop the ability to critically analyze these texts. Have developed oral comprehension skills and adequate attendance of English language lectures and seminars by foreign visitors to the Department. Have developed sufficient individual, academic research skills to fulfill their respective obligations in the Computer Science and Engineering curriculum

Course Content: Computer Science and Engineering Degree - what does it mean? The Cyprus University of Technology - identity. Take notes. Types of learning. Bibliographic references, paraphrases, plagiarism. Write a summary. Library use skills. Dictionary skills (monolingual, bilingual). Career in Computer Engineering. Familiarity with textiles from Computer Science. Digital knowledge. The Information Age. Graphical User Interfaces. Multimedia. Ducts. The Internet. Communication systems. Robotics.

LCE 116 English for Computer Science and Engineering II

Purpose and Objectives: General objective of the second part of the special course on Computer Engineers is to enable students to communicate competently at a B1-B2 level of the Common European Framework of Reference (CEFR) for Languages on issues related to students’ social and student life (university, curriculum, field of study), as well as on issues related to their future professional careers (occupations, short CV, etc.). Computer Assisted Language Learning (CALL) and culture awareness development are done through communicative situations deriving from authentic-like communication situations students may experience in their lives, during their studies and professional careers. Language competence is acquired through the use of text types, scenarios, and roles which promote the production and understanding of spoken and written language related to the topics covered. The course is based on post communicative era method, a student-centered, blended learning approach, and aims to familiarize students with authentic reading material related to general topics such as university student life, learning styles and language learning, academic topics such as how to study effectively, take notes and use the appropriate reference style and to specific topics related to the field of Electrical Engineering and Computer Science and Engineering. The program builds on contemporary learning theories such as constructivism and social constructivism and student-centered teaching methods and develops both student language skills and transferable 21st century skills such as communication, use of new information and communication technologies, collaboration, creativity and innovation, critical thinking and intercultural awareness as well as skills such as autonomous and lifelong learning. In particular, the course in Computer Science and Engineering aims to make students capable of managing English Bibliography, as well as understanding and editing material related to Computer Science and Engineering.

Learning Outcomes: By the end of the course, students shall be able to: Derive communicative circumstances, scenarios and roles through authentic situations of their social and student life (university, building access guidelines, their field of study), as well as topics related to their future career (workplace, occupations) industry, duties). Communicate with sufficient level B1-B2 proficiency of the Common European Framework of Reference (CEFR) for the languages (production and comprehension of oral and written language) on the above topics, through relevant and relevant textual types, scripts, and roles. Understand English-language written texts on Computer Science, Engineering, various new technologies, and begin to develop the ability to critically analyze these texts. Have developed oral comprehension skills and adequate attendance of English language lectures and seminars by foreign visitors to the Department. Have developed sufficient individual academic research skills to fulfill their respective obligations in the Computer Science and Engineering curriculum

Course Content: Computer architecture; Computer applications; Data Security; Computer Support; Peripheral devices; Websites; Artificial Intelligence; Hackers and viruses

LCE 116 English for Computer Scientists and Engineers II

Course Title	English for Computer Scientists and Engineers II
Course Code	LCE 116
Course Type	Compulsory
Level	Undergraduate
Year / Semester	1st / 2nd
Teacher’s Name	-
ECTS	4	Lectures / week		2 (2 hours)		Laboratories / week	-
Course Purpose and Objectives	General objective of the second part of the special course on Computer Engineers is to enable students to communicate competently at a B1-B2 level of the Common European Framework of Reference (CEFR) for Languages on issues related to students’ social and student life (university, curriculum, field of study), as well as on issues related to their future professional careers (occupations, short CV, etc.). Computer Assisted Language Learning (CALL) and culture awareness development are done through communicative situations deriving from authentic-like communication situations students may experience in their lives, during their studies and professional careers. Language competence is acquired through the use of text types, scenarios, and roles which promote the production and understanding of spoken and written language related to the topics covered. The course is based on post communicative era method, a student-centered, blended learning approach, and aims to familiarize students with authentic reading material related to general topics such as university student life, learning styles and language learning, academic topics such as how to study effectively, take notes and use the appropriate reference style and to specific topics related to the field of Electrical Engineering and Computer engineering and Informatics. The program builds on contemporary learning theories such as constructivism and social constructivism and student-centered teaching methods and develops both student language skills and transferable 21st century skills such as communication, use of new information and communication technologies, collaboration, creativity and innovation, critical thinking and intercultural awareness as well as skills such as autonomous and lifelong learning. In particular, the course in Computer Engineering and Informatics aims to make students capable of managing English Bibliography, as well as understanding and editing material related to Computer Science and Informatics.
Learning Outcomes	By the end of the course, students shall be able to: Derive communicative circumstances, scenarios and roles through authentic situations of their social and student life (university, building access guidelines, their field of study), as well as topics related to their future career (workplace, occupations) industry, duties). Communicate with sufficient level B1-B2 proficiency of the Common European Framework of Reference (CEFR) for the languages (production and comprehension of oral and written language) on the above topics, through relevant and relevant textual types, scripts, and roles. Understand English-language written texts on Computer Engineering and Informatics, Engineering, various new technologies, and begin to develop the ability to critically analyze these texts. Have developed oral comprehension skills and adequate attendance of English language lectures and seminars by foreign visitors to the Department. • Have developed sufficient individual academic research skills to fulfill their respective obligations in the Electrical Engineering curriculum
Prerequisites	LCE 115		Required		None
Course Content	Computer architecture Computer applications Data Security Computer Support Peripheral devices Websites Artificial Intelligence Hackers and viruses
Teaching Methodology	Interactive lectures of various types. Individual, group or collective comprehension and creation of verbal and written textual types of authentic type, specific communication circumstances. Active lifelong participation in interactive learning activities in a variety of forms. Active participation in interactive learning activities in a variety of forms, assisted by new information and communication technologies. Independent study.
Bibliography	Required Textbook: Eric H.Glendinning& John McEwan Oxford English for Information Technology Oxford University Press (ISBN: 978-0-19-457492-1 Additional material (articles, brochures and electronic materials) will be provided to students for support. Websites: https://spectrum.ieee.org/ https://www.eetimes.com/ http://www.electricalengineering4u.com/ Recommended reinforcing material: Printed Dictionaries: Longman Dictionary of Contemporary English. Pearson Longman. Cambridge’s Advanced Learner’s Dictionary. Cambridge University Press. Concise Oxford English Dictionary. Oxford University Press. Online dictionaries: Cambridge dictionaries online: http://dictionary.cambridge.org/ The free online dictionary with sound: http://www.thefreedictionary.com/ Greek-English, English-Greek online dictionary: http://www.in.gr/dictionary/ Dictionary.com: http://dictionary.reference.com/ Merriam-Webster online dictionary: http://www.merriam-webster.com/home.htm English Pronouncing Dictionary with Instant Sound http://www.howjsay.com/
Assessment	• Final examination 30% • Midterm examination 20% • Classroom exercises 25% • Homework 25%
Language	English

ΕEI 203 Introduction to Biology

ΕEI 101 Advanced Mathematics I

Τίτλος Μαθήματος	Προχωρημένα Μαθηματικά Ι
Κωδικός Μαθήματος	ΕEI 101
Τύπος μαθήματος	Υποχρεωτικό
Επίπεδο	Προπτυχιακό
Έτος / Εξάμηνο φοίτησης	1o / 1o
Όνομα Διδάσκοντα	Ειδικός Επιστήμονας
ECTS	5	Διαλέξεις / εβδομάδα		2 (2 ώρες)	Εργαστήρια / εβδομάδα		-
Στόχοι Μαθήματος	Σκοπός του μαθήματος Προχωρημένα Μαθηματικά Ι είναι η αφομοίωση από τους φοιτητές των βασικών εννοιών του απειροστικού και ολοκληρωτικού λογισμού πραγματικών συναρτήσεων μιας μεταβλητής και η εφαρμογή τους σε προβλήματα της επιστήμης του μηχανικού.
Μαθησιακά Αποτελέσματα	Αναπτύσουν μια ισχυρή βάση στον λογισμό για περαιτέρω σπουδές στην επιστήμη και τη μηχανική Κατανοούν και να εφαρμόζουν τις έννοιες των ορίων, της συνέχειας και των παραγώγων Εφαρμόζουν τεχνικές παραγώγισης για την ανάλυση συναρτήσεων και την επίλυση προβλημάτων που αφορούν τα μέγιστα, τα ελάχιστα και την κοιλότητα Εκτελούν ολοκλήρωση χρησιμοποιώντας τυπικές μεθόδους και εφαρμόζουν συγκεκριμένα ολοκληρώματα σε περιοχές, όγκους και μήκη καμπυλών Να κατανοούν και να εφαρμόζουν τις έννοιες των ακολουθιών, των σειρών και των δοκιμών σύγκλισης Χρησιμοποιούν δυναμοσειρές, σειρές Maclaurin και Taylor για προσεγγίσεις και επίλυση διαφορικών εξισώσεων Εφαρμόζουν βασικές τεχνικές επίλυσης διαφορικών εξισώσεων πρώτου βαθμού Κατανοούν τις βασικές αρχές των μιγαδικών αριθμών και τις εφαρμογές τους
Προαπαιτούμενα	-		Συναπαιτούμενα			-
Περιεχόμενο Μαθήματος	Συναρτήσεις μιας μεταβλητής: Εισαγωγικές έννοιες Σύνολα, Διαστήματα, Ένωση και τομή συνόλων. Ορισμός συνάρτησης. Πεδίο ορισμού και πεδίο τιμών. Γραφική παράσταση συνάρτησης. Πράξεις πραγματικών συναρτήσεων. Σύνθεση πραγματικών συναρτήσεων και πεδίο ορισμού. Σύνθετες συναρτήσεις. Αντίστροφές συναρτήσεις και πεδίο ορισμού. Όρια: Ορισμός. Πλευρικά όρια. Βασικά θεωρήματα. Άπειρα όρια. Όρια συνάρτησης όταν το χ τείνει στο . Όρια βασικών συναρτήσεων. Ιδιότητες Συνέχεια συναρτήσεων: Ορισμός. Συνέχεια συνάρτησης σε ανοιχτό και κλειστό διάστημα. Είδη ασυνέχειας. Ιδιότητες και θεωρήματα συνεχών συναρτήσεων. Όριο και Συνέχεια σύνθεσης συναρτήσεων. Παραγώγιση: Ορισμός. Παραγώγου. Παράγωγοι Βασικών συναρτήσεων. Βασικοί Κανόνες Παραγώγισης. Παράγωγος Σύνθετης Συνάρτησης. Παράγωγοι Πεπλεγμένων Συναρτήσεων. Παράγωγος συναρτήσεων σε παραμετρική μορφή. Εφαρμογές Παραγώγισης : Μονοτονία Συναρτήσεως. Τοπικά Ακρότατα. Απόλυτα ακρότατα. Κυρτές και Κοίλες Συναρτήσεις. Σημεία Καμπής. Κανόνες de l'Hospital. Ολοκληρώματα : Αόριστο ολοκλήρωμα. Βασικοί τύποι ολοκληρωμάτων. Ιδιότητες του Αορίστου ολοκληρώματος. Τεχνικές ολοκλήρωσης: Μέθοδος της αντικατάστασης. Ολοκλήρωση ρητών συναρτήσεων - Mερικά κλάσματα. Ολοκλήρωση κατά παράγοντες Ορισμένο ολοκλήρωμα και εφαρμογές : Υπολογισμοί εμβαδών. Υπολογισμοί όγκων εκ περιστροφής. Μιγαδικοί αριθμοί: Ορισμοί. Σχεδιάγραμμα του Argand. Πράξεις Μιγαδικών αριθμών σε Καρτεσιανή μορφή, Πολική και Εκθετική μορφή. Μετατροπή μιγαδικών αριθμών από μια μορφή σε άλλη. Αλγεβρικές πράξεις μιγαδικών αριθμών. Διαφορικές Εξισώσεις 1ης Τάξης: Εξισώσεις Χωριζόμενων Μεταβλητών. Ομογενείς Διαφορικές Εξισώσεις. Ακριβείς Διαφορικές Εξισώσεις. Γραμμικές Διαφορικές Εξισώσεις Η έννοια της σειράς. Σύγκλιση σειράς. Κριτήρια Σύγκλισης . Δυναμοσειρές. Σειρές Taylor. Σύγκλιση Δειναμοσειρών. Παραγώγιση και Ολοκλήρωση Δειναμοσειρών.
Μεθοδολογία Διδασκαλίας	Διαλέξεις και φροντιστήριο
Βιβλιογραφία	R.L.Finney, M.D. WEIR, F.R. Giordano: THOMAS ΑΠΕΙΡΟΣΤΙΚΟΣ ΛΟΓΙΣΜΟΣ (Τόμοι Ι & ΙΙ), Απόδοση στα ελληνικά Μανώλης Αντωνογιαννάκης, Πανεπιστημιακές Εκδόσεις Κρήτης, Ηράκλειο Κρήτης 2004. Michael Spivak: ΔΙΑΦΟΡΙΚΟΣ ΚΑΙ ΟΛΟΚΛΗΡΩΤΙΚΟΣ ΛΟΓΙΣΜΟΣ - Μια εισαγωγή στην Ανάλυση, Πανεπιστημιακές Εκδόσεις Κρήτης, Ηράκλειο Κρήτης 2004. H. Anton, I. Bivens, S. Davis, CALCULUS: EARLY TRANSCENDENTALS COMBINED, John Wiley. ΣΤΕΦΑΝΟΣ ΤΡΑΧΑΝΑΣ: ΣΥΝΗΘΕΙΣ ΔΙΑΦΟΡΙΚΕΣ ΕΞΙΣΩΣΕΙΣ – Μέθοδοι Λύσης και Εφαρμογές , Πανεπιστημιακές Εκδόσεις Κρήτης, Ηράκλειο Κρήτης 2004. J. Polking, A.Boggess, D.Arnold: ORDINARY DIFFERENTIAL EQUATIONS, Prentice Hall. P.W.Davis: DIFFERENTIAL EQUATIONS FOR MATHEMATICS, SCIENCE AND ENGINEERING, Prentice Hall. J.MATTHEWS, R. HOWELL: COMPLEX ANALYSIS FOR MATHEMATICS AND ENGINEERING, Jones & Bartlett. R.Churchill- J. Brown: ΜΙΓΑΔΙΚΕΣ ΣΥΝΑΡΤΗΣΕΙΣ ΚΑΙ ΕΦΑΡΜΟΓΕΣ, Απόδοση στα ελληνικά Δημήτρης Καραγιαννάκης, Πανεπιστημιακές Εκδόσεις Κρήτης, Ηράκλειο Κρήτης 2001. http: // mathworld.wolfram.com http: // en.wikipedia.org/wiki/Calculus
Αξιολόγηση	Ενδιάμεση εξέταση (40%), Τελική εξέταση (60%)
Γλώσσα	Ελληνική

ΕEI 102 Physics I

Course Title	Physics I
Course Code	EEI 102
Course Type	Compulsory
Level	Undergraduate
Year / Semester	1st / 1st
Teacher’s Name	Special Scientist
ECTS	5	Lectures / week		2 (2 hours)	Laboratories / week
Course Purpose and Objectives	The main objective of the course is to ensure a broad introduction to physics at the academic level and to develop students' natural intuition in macroworld problems. The specific objectives of the course are: Providing students with knowledge of Newtonian Engineering. Students will acquire several problem-solving skills while developing their physical intuition. Introduction to the Basic Principles of Thermodynamics so that students are thoroughly familiar with key concepts such as heat, temperature, thermal properties and the three thermodynamic axioms.
Learning Outcomes	Upon successful completion of the course, students shall: Apply Newtonian Mechanics principles to solve physics problems Apply the basic principles of Thermodynamics to analyze heat, temperature, and thermal processes Develop problem-solving skills and physical intuition for modeling real-world phenomena Recognize the importance of physics in understanding natural phenomena and engineering applications
Prerequisites	-		Required			-
Course Content	Kinematic, Newton's Laws, Projectile motions, Energy, Energy Conservation. Momentum and Collisions. Solid body rotation, rotating reference systems. Solid body movement. Gravitational attraction. Oscillations. Mechanics of fluids. Wave motion. Temperature. Ideal Gases, First Thermodynamic Law, Kinetic Gas Theory, Thermal Engines, Entropy, Second Thermodynamic Law, Third Thermodynamic Law.
Teaching Methodology	Lectures / Exercises
Bibliography	University Physics (volume 1), D. Young Hugh. Physics for Scientists and Engineers (Volume 1), Sherway. (ISBN: 1133954154) Physics (Unified), D. Halliday, R. Resnick and J. Walker, Gutenberg editions. (ISBN: 0471758019) Physics: Principles with Applications (6th Edition), Douglas C. Giancoli. (ISBN: 0136073026)
Assessment	Homeworks: 0%. Quizzes: 10%. Five tests lasting 10 minutes, from 2% of the total score each. Midterm examination: 30%. Final examination: 60%.
Language	Greek

ΕEI 103 Advanced Mathematics II

Course Title

Advanced Mathematics II

Course Code

EEI 103

Course Type

Compulsory

Level

Undergraduate

Year / Semester

1st / 2nd

Teacher’s Name

Special Scientist

ECTS

Lectures / week

2 (2 hours)

Laboratories / week

Course Purpose and Objectives

To understand the importance and role of Calculus and Differential Equations in all the sciences and especially in Electrical Engineering, Computer Engineering and Computer Science, as well as to become fluent in the various techniques for solving problems in these areas.

Learning Outcomes

It is expected that the student will be able to:

Demonstrate knowledge of the theoretical foundations of calculus and differential equations, including definitions, properties, and theorems
Translate relations between variable quantities in a problem into mathematical terms
Construct and solve the differential equations governing a specific problem, and assess solution validity based on physical laws
Define and graph functions of several variables, calculate their limits, and determine continuity
Calculate partial derivatives of multivariable functions and apply them to optimization problems
Perform multiple integrals and apply them to areas, volumes, mean values, and surface areas

Prerequisites

Required

Course Content

I. Differential Equations:

Basic characteristics of differential equations (DE).
Simple DE of first and second order, applications (Newton’s equations.)
General study of linear DE.
Linear DE with variable coefficients, method of power series (applications to Bessel and Legendre equations).

II. Calculus:

Functions of several variables.
Partial derivatives.
Applications of partial derivatives.
Multiple integrals.
Applications of integrals.

Teaching Methodology

Theory exposition (lecture, use of examples and case studies).

Problem solving (lab, presentation of solved exercises, group discussion).

Bibliography

Glyn James, Advanced Modern Engineering Mathematics (3rd Edition), Prentice Hall, ISBN-10: 0130454257
Erwin Kreyszig, Advanced Engineering Mathematics (8th Edition), John Wiley & Sons, ASIN: B000KCUAK8
Τραχανάς Στέφανος, Συνήθεις Διαφορικές Εξισώσεις (7η έκδοση), Πανεπιστημιακές Εκδόσεις Κρήτης, ISBN 960-524-089-0
W Boyce, R DiPrima, Elementary Differential Equations and Boundary Value Problems, John Wiley and Sons, ISBN 978-0-471-43338-5
P. W. Davis, Differential Equations for Mathematics and Engineering, Prentice Hall
H. Anton, I. Bivens, S. Davis, Calculus: Early Transcendentals Combined, John Wiley
M. Spivak (A. Γιαννόπουλος, Δ. Καραγιαννάκης, Μ. Λάμπρου) Διαφορικός και ολοκληρωτικός λογισμός: μια εισαγωγή στην ανάλυση, Πανεπιστήμιο Κρήτης
Παύλος Χριστοδουλίδης & Χρυστάλλα Δημητριάδη, Σημειώσεις διαλέξεων στις ΔΙΑΦΟΡΙΚΕΣ ΕΞΙΣΩΣΕΙΣ, Τεχνολογικό Πανεπιστήμιο Κύπρου
Παύλος Χριστοδουλίδης, Σημειώσεις διαλέξεων στον Απειροστικό Λογισμό, Τεχνολογικό Πανεπιστήμιο Κύπρου
http://mathworld.wolfram.com
http://en.wikipedia.org/wiki/Differential_equation
http://en.wikipedia.org/wiki/Calculus

Assessment

Assignment	10%
Midterm exams (written)-2	40%
Final exam (written)	50%

Language

Greek

ΕEI 104 Linear Algebra

Course Title	Linear Algebra
Course Code	EEI 104
Course Type	Compulsory
Level	Undergraduate
Year / Semester	1st / 2nd
Teacher’s Name	Paul Christodoulides
ECTS	5	Lectures / week		2 (2 hours)	Laboratories / week		-
Course Purpose and Objectives	To give a broad knowledge as to basic concepts of linear algebra in relation to matrices and solutions of linear systems as well as basic complex analysis, which can become tools of better understanding and solving related engineering applications.
Learning Outcomes	Upon successful completion of the course, the student will have the knowledge and skills required to: Solve linear systems using methods such as Gaussian elimination, Cramer’s rule, and matrix inverses Compute determinants and inverses of matrices Define fundamental subspaces of a matrix, determine dimension, eigenvalues, and eigenvectors Represent linear transformations with matrices and determine kernel and image Perform operations with complex numbers and convert them into polar form Calculate limits and determine continuity of complex functions Define and compute power series and Laurent series expansions, and evaluate residues Evaluate complex integrals using the residue theorem
Prerequisites	-		Required			-
Course Content	Matrix algebra: definitions, properties, special matrices, Determinants, Sarrus rule, cofactors, row reduction, Inverse matrix, systems of matrix equations, Systems of linear equations, Cramer’s rule, Gaussian reduction, matrix method, Vectors: definitions, properties, Linear spaces: subspaces, linear Independence, basis, rank and nullity, Linear transformations, The eigenvalue problem: eigenvalues, eigenvectors, quadratic forms, Linear algebra applications, Singular value decomposition, Complex numbers: algebra, geometry, coordinate systems, Complex functions: definitions, limits, differentiation, power series functions, integration, Contour integrals, residuals.
Teaching Methodology	Presentations, Solution of exercises, Group discussion
Bibliography	H. Anton, C. Rorres, Elementary Linear Algebra Applications Version, John Wiley (ISBN: 1118434412) B. Kolman, D.R. Hill, Elementary Linear Algebra with Applications, Prentice Hall (ISBN: 0132296543) G. Strang (Π. Πάμφιλος), Γραμμική άλγεβρα και εφαρμογές, Πανεπιστημιακές Εκδόσεις Κρήτης J. Matthews, R. Howell, Complex Analysis for Mathematics and Engineering, Jones & Bartlett (ISBN: 0763737488) Π. Χριστοδουλίδης, Σημειώσεις Διαλέξεων στη Γραμμική Άλγεβρα Π. Χριστοδουλίδης, Σημειώσεις Διαλέξεων στη Μιγαδική Ανάλυση
Assessment	Mid-semester exam (30%), Assignments (20%), Final exam (50%)
Language	Greek

ΕEI 231 Signals and Systems

Course Title	Signals and Systems
Course Code	EEI 231
Course Type	Mandatory
Level	BSc Degree
Year / Semester	2nd / Spring
Teacher’s Name	Paul Christodoulides
ECTS	6	Lectures/Week		2	Lectures/Week		1
Course Purpose and Objectives	To give a wide knowledge to engineers as to basic concepts on signals and systems as well as the connected transformations, which can become tools for better understanding and solving related applications
Learning Outcomes	Acquiring fundamental knowledge of signals and systems for use in engineering applications Apply Fourier series and Fourier transforms to analyze continuous signals Apply Fourier transforms to analyze discrete-time signals Use Laplace transforms for system analysis and problem solving Apply z-transforms to discrete-time systems
Prerequisites	-		Required			-
Course Content	Introduction to signals, Introduction to systems, Fourier expansion and series, Fourier transform and applications, Fourier transform of discrete signals, Laplace transform and applications, z transform.
Teaching Methodology	Presentations, Solution of exercises, Group discussion
Bibliography	V. Oppenheim, A. S. Willsky, S. H. Nawab, Signals and systems Version, Prentice Hall (ISBN: 9572181424) L. Philips, J. M. Parr, E. A. Riskin, Signals, Systems and Transforms, Prentice Hall F. J. Taylor, Principles of Signals and Systems, McGraw Hill Π. Χριστοδουλίδης, Σημειώσεις Διαλέξεων στην Ανάλυση Fourier και τους Μετασχηματισμούς (in Greek)
Assessment	Mid-semester exam (30%), Assignments and MATLAB (25%), Final exam (45%)
Language	Greek

ΕΕΙ 233 Computer Organization

Course title	Computer Organization
Course code	EEI 233
Course type	Mandatory
Level	Undergraduate
Year / Semester	2nd year/4th semester
Teacher’s name	Andreas Diavastos
ECTS	6	Lectures / week	2x2h	Laboratories / week		1x2h
Course purpose and objectives	The course presents in detail the basic operating structure of computer systems with a single core processor and introduces computer engineering. This course answers the question "How" a computer system is implemented and focuses on the physical implementation and the actual details of the hardware that implement architectural concepts/techniques. It presents the basic building blocks of a computer system: arithmetic and logic units, control units, memory system, input/output system, data bus. It presents the RISC Central Processing Unit (CPU) using the MIPS architecture and the RISC ISA (Instruction Set Architecture) as a basis. It presents computer arithmetic, pipelining, the memory hierarchy (Main Memory, Cache, Virtual Memory). And finally, it presents the input/output units of a computer (Storage, Networks, Peripherals).
Learning outcomes	Upon completion of the course, students will be able to: Describe the basic building blocks of a computer, such as the Central Processing Unit (CPU), memory, input/output devices, and how these components interact to perform calculations. Program in Assembly language, which is a symbolic representation of machine code. Describe the various levels of memory in a computer, such as cache, main memory, and secondary storage, and how these levels work together to provide fast and efficient access to data. Describe the various technologies used to connect peripheral devices, such as keyboards, mice, monitors, and printers, to the computer. Answer the question "How" is a computer system implemented and operates in hardware.
Prerequisites	CSE 132 - Introduction to Computing and Programming in C (CSE-degree), EEN 212 - Digital Systems (EE-degree)		Required		-
Course content	The course covers the following content: Abstraction and Computer Technology Machine Language Instructions, MIPS Computer Arithmetic Integer and Floating Point Numbers Addition, Subtraction, Multiplication, Division Computer Performance Analysis Central Processing Unit (CPU) Datapath and Control Pipelining Data, Structural and Control Hazards Memory Hierarchy Main Memory, Cache Memory, Virtual Memory Input/Output Devices Permanent Storage, Interfacing with CPU and Other Peripherals
Teaching methodology	Presentations conducted by the instructor using slides, while the lab sessions, with practical programming assignments conducted in a MIPS assembly language emulator, are administered by special teaching staff under the supervision of the class instructor (assignments prepared by the class instructor). During lectures, often an in-depth discussion with the class instructor and a questions-and-answers session is carried out. Small in-class exercises are given every week to students to help with engagement and to help them to better understand and grasp the concepts being taught. Enough time is given to students to work on solutions to these exercises and discuss them with the instructor during the lecture/lab and at the end an indicative solution is presented and explained to them.
Bibliography	D. A. Patterson and J. L. Hennessy, Computer Organization and Design: The Hardware/Software Interface, Morgan Kaufman, 3rd Ed., Revised 2007, ISBN-10: 0123706068 David A. Patterson, John L. Hennessy, Computer Organization and Design MIPS Edition: The Hardware/Software Interface, Morgan Kaufman, 6th Edition, ISBN-10: 9780128201091 John L. Hennessy and David A. Patterson, Computer Architecture: A Quantitative Approach, 6th Edition, Morgan Kaufman. ISBN: 978-0-12-383872-8
Assessment	50% Final Exam: Covers the entire material taught during the semester 30% Quizzes: 3 quizzes during the entire semester, one each month of completed material. Each quiz covers only the material covered during that month. Each quiz takes 10%. 15% Lab assignments: 3 lab assignments during the entire semester, one each month. The students must deliver and present their solutions. 5% Weekly homework
Language	Greek (the class textbook is written in English, but class notes are in Greek). Some content of the class presentations, for example various technical definitions and terms, are presented in mixed-mode language, mostly in Greek (the Greek language lacks modern technical terms and hence referring to them in English is unavoidable – this is also carried out as a better preparation of students in the wider international professional scene).

ΕΕΙ 234 Probabilities and Random Processes

Course title

Probabilities and Random Processes

Course code

EEI 234

Course type

Mandatory

Level

Undergraduate

Year / Semester

3rd year/5th semester

Teacher’s name

Michalis Michaelides

ECTS

Lectures / week

Laboratories / week

Course purpose and objectives

The course provides a broad and systematic introduction to the basic principles and advanced concepts of Probability Theory and Random Processes, with particular emphasis on the tools and applications needed in the field of Electrical Engineering and Computer Engineering and Informatics.

Learning outcomes

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Prerequisites

Required

Course content

Topics covered:

<>·o·Important Random Variables and their Properties. Discrete: Bernoulli, Binomial, Poisson, Geometric. Continuous: Uniform, Normal, Exponential, Gamma. Central Limit Theorem

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Teaching methodology

Lectures, tutorials, discussion

Bibliography

Alberto Leon-Garcia, “Probability, Statistics, and Random Processes for Electrical Engineering”, Pearson International, Third Edition, 2009 (ISBN: 0132336219)
R. Walpole, R. Myers, S. Myers and K. Ye Probability & Statistics for Engineers & Scientists, 9th Edition, Pearson Prentice Hall, 2012 (ISBN: 0321629116)
A. Papoulis, S. U. Pillai, Probability, random variables, and stochastic processes, Boston: McGraw-Hill, 2002. (ISBN: 0073660116)
S. Ross, A first course in probability, 8th Edition, Pearson Education, 2011 (ISBN: 013603313X)
Lecture Notes.

Assessment

Assignments	15%
Midterm	35%
Final Exam	50%

Language

Greek

ΕΕΙ 331 Probabilities and Random Processes

ΕΕΝ 442 Power System Analysis and Protection

Course title

Power System Analysis and Protection

Course code

ΕΕΝ 442

Course type

Elective

Level

Undergraduate

Year / Semester

4rd year/8th semester

Teacher’s name

Petros Aristidou

ECTS

Lectures / week

Laboratories / week

Course purpose and objectives

This course extends into the field of electric power systems based on the course EEN320. It focuses on the use of computational methods for the analysis of electric power systems in steady-state as well as during symmetrical and asymmetrical faults. It proceeds to provide the fundamental understanding of protection devices operation and selection.

Learning outcomes

On completion of this course, students should be able to:

Understand the fundamental computational methods to analyze the steady-state and under-fault operation of electric power systems;
Understand and analyze the basic power transfer limits in electric power systems; and,
Understand the fundamentals of protection devices and protection selection.

Prerequisites

EEN 320 - Introduction to Electric Power Systems

Required

Course content

The course consists of these parts:

Revision of power engineering fundamentals (per-unit, line/transformer/generator/load models)
Fundamentals of power system operation (Surge impedance loading, lossless line, efficiency, loadability, maximum power over line, P-δ and P-V characteristics, Ferranti effect)
Power flow analysis (nodal admittance matrix, NR-method, fast decoupled, DC power flow)
Unbalanced operation (symmetrical components)
Fault analysis (modeling line/transformer/generator during fault, solid faults)
Protection fundamentals (fundamentals, structure, overcurrent, distance, differential, digital relays, protection design and coordination)

Teaching methodology

Lectures, in-class exercises, software and hardware laboratory exercises

Bibliography

A. Gómez-Expósito, A. J. Conejo, and C. A. Canizares, Electric Energy Systems Analysis and Operation, 2nd edition, CRC Press, 2018. (ISBN: 9781138724792)
D. Glover, M. S. Sarma and T. Overbye, Power System Analysis & Design, 6th edition, Cengage Learning, 2017. (ISBN: 130563618X)
Nasser Tleis, Power Systems Modelling and Fault Analysis : Theory and Practice, 2nd edition, Academic Press, 2019. (ISBN: 012815117X)
Ν. Βοβός, Γ. Γιαννακόπουλος, “Ανάλυση Συστημάτων Ηλεκτρικής Ενέργειας”, Β’ έκδοση, εκδόσεις ΖΗΤΗ, 2019
Lecture notes

Assessment

Mid-semester test	20%
Software Lab Exam	20%
Hardware Lab Exam	20%
Final exam	40%

Language

Greek

ΕΕΝ 443 Power Electronics

Course title

Power Electronics

Course code

ΕΕΝ 443

Course type

Elective

Level

Undergraduate

Year / Semester

4rd year/8th semester

Teacher’s name

Neophytos Lophitis

ECTS

Lectures / week

Laboratories / week

Course purpose and objectives

This course provides an advanced understanding of power electronic systems, covering power semiconductor devices, converter topologies, circuit analysis, and practical applications. Building on prior knowledge of electronics and circuit theory, students will develop the ability to analyse, design, and optimise power electronic circuits.

A problem-based learning approach, combined with simulations, enables students to apply theoretical concepts to practical scenarios, solve complex engineering problems, and validate circuit designs. Through a blend of theoretical analysis, problem solving, and hands-on application, students will gain a comprehensive perspective on power electronics, preparing them for both industry and research roles.

With a strong emphasis on analytical thinking and simulation-driven design, this course equips students with the expertise needed to tackle real-world engineering challenges in power electronics.

Objectives

Introduce students to the fundamental concepts and techniques used in modern power electronics.
Provide insight into the selection and application of semiconductor power devices in real-world systems.
Familiarise students with typical power conversion circuits and their role in electrical energy processing.
Encourage the use of simulation tools as part of the design and analysis process for power electronic circuits.
Expose students to the challenges and considerations in designing efficient and reliable power converters.
Provide an overview of emerging trends and technologies shaping the future of power electronics.

Learning outcomes

By the end of this course, students will be able to:

Evaluate the properties, operation, and performance trade-offs of power semiconductor devices, including diodes, MOSFETs, IGBTs, and wide bandgap semiconductors.
Apply the principles of power semiconductor switches to design and optimise power electronic circuits.
Analyse different power conversion topologies (AC/DC, DC/DC, DC/AC, AC/AC, and active filters) and assess their efficiency and performance in various applications.
Interpret and generate waveforms and current characteristics to assess the behaviour of power electronic circuits.
Use advanced simulation tools (e.g., SPICE-based modelling) to design, verify, and optimise power electronic devices or systems.
Solve complex engineering problems that integrates theoretical concepts with practical implementation.
Design and assess power electronic circuits for renewable energy applications, particularly in photovoltaic and wind turbine systems.
Investigate and discuss emerging trends and advancements in power electronics, including novel semiconductor technologies and system architectures.

Prerequisites

EEN 312-Electronics II

Required

Course content

Semiconductor Power Switches

Power semiconductor materials and properties
Operation and characteristics and performance metrics for switching devices
The ideal power switch and fundamentals of Power Semiconductor Devices
Power Diodes and Rectifiers
MOSFETs for Power Electronics
Insulated Gate Bipolar Transistors (IGBTs)
Wide Bandgap Semiconductors

Inverter Topology and Function

Five Basic Power Electronics Circuits (AC / DC, DC / DC, DC / AC, AC / AC and Active Filters

Generation and description of relevant waveforms and currents.

Power Circuit Analysis

Spice modelling, simulation and analysis of power electronic circuits

Applications

Limited presentation of applications in Photovoltaics and wind turbine systems

New Trends

Descriptively new trends and new systems in Power Electronics

Teaching methodology

The module is delivered through a combination of interactive lectures, problem-solving sessions, and computer-based laboratory work. Lectures are used to introduce key theoretical concepts, device principles, and circuit topologies, providing the necessary foundation for further application. Problem-solving sessions focus on applying theory to practical engineering problems, helping students develop critical thinking and analytical skills.

The computer laboratory sessions integrate simulation-based tasks where students design, analyse, and validate power electronic circuits, creating a direct link between theoretical knowledge and practical implementation. The methodology promotes an active learning environment where students progressively build their expertise through a blend of theory, application, and hands-on simulation exercises.

Bibliography

N. Mohan and S. Raju, Power Electronics, A First Course. John Wiley & Sons, 2023. (ISBN: 1119818567)
N. Mohan, T. M. Undeland, and W. P. Robbins, Power Electronics: Converters, Applications, and Design. John Wiley & Sons, 2003. (ISBN: 9780471226932)
Mohan, Ned. and Undeland, Tore M, Ηλεκτρονικά ισχύος. Α. Τζιόλα, 1996.

Assessment

Progress Assessment	25%
Laboratory Assessment	25%
Final exam	50%

Language

Greek

ΕΕΝ 445 Renewable Energy Sources

Course title

Renewable Energy Sources

Course code

ΕΕΝ 445

Course type

Elective

Level

Undergraduate

Year / Semester

4rd year/8th semester

Teacher’s name

Neophytos Lophitis

ECTS

Lectures / week

Laboratories / week

Course purpose and objectives

This course is designed to provide engineering students with an in-depth and critical understanding of renewable energy technologies, with a strong focus on photovoltaic and wind energy systems. The course aims to develop the ability to critically assess, design, and implement renewable energy systems, while addressing real-world engineering constraints and sustainability considerations.

The main objectives are to:

Deepen students' theoretical and practical understanding of key renewable energy technologies.
Develop critical thinking and design capabilities related to the analysis and integration of renewable energy systems.
Enable students to interpret and apply complex engineering principles for the development of efficient and sustainable energy solutions.
Foster the ability to evaluate system performance, considering both technical and environmental factors.
Expose students to modern energy storage technologies and hybrid system configurations.

Learning outcomes

Upon successful completion of the course, students will be able to:

Acquire in-depth knowledge of the analysis, design, and implementation of photovoltaic and wind turbine systems.
Critically assess the operation, performance, and integration of PV and wind energy systems.
Develop technical specifications and conduct system-level assessments under varying conditions.
Design and optimise renewable energy systems using advanced engineering methods.
Use simulation and design tools to model and analyse system behaviours.
Evaluate and improve the performance of PV and wind systems.
Compare and select appropriate energy storage solutions for integration with renewable systems.
Propose innovative solutions to enhance the functionality and resilience of renewable energy systems, in line with emerging trends.

Prerequisites

Required

Course content

Photovoltaic Power Systems

The photovoltaic effect and electrical energy conversion principles.
PV panels: construction, electrical characteristics, and array configurations.
Design and implementation of photovoltaic arrays.
Electronic converters and their role in PV systems.
Maximum Power Point Tracking (MPPT) techniques.

Wind Power Systems

Wind energy fundamentals and wind turbine types.
Power extraction principles and efficiency considerations.
Converter systems for wind turbines.
MPPT strategies for wind power applications.
Electrical machines for wind applications

Other Renewable Energy Technologies and Energy Storage

Overview of additional renewable technologies (e.g., small hydroelectric systems).
Battery technologies and other electric storage systems

Teaching methodology

The course combines lectures, problem-solving sessions, simulation-based labs, and hands-on experimental work. Lectures cover core theory, while problem-solving sessions develop analytical and design skills. In PC labs, students use simulation tools to model and optimise renewable energy systems. In experimental labs, students build and test real-world components and subsystems, linking theory to practice. This integrated approach fosters both technical knowledge and practical competencies, preparing students to tackle real engineering challenges in renewable energy.

Bibliography

Robyns, B. Electricity Production from Renewable Energies, ISTE, 2021. (ISBN: 1786306611)
Mohan, N. and Raju, S. Power Electronics: A First Course, John Wiley & Sons, 2023. (ISBN: 1119818567)
Mohan, N., Undeland, T. M., and Robbins, W. P. Power Electronics: Converters, Applications, and Design, John Wiley & Sons, 2003. (ISBN: 9780471226932)
Walter John and Nicholas Jeckings, Αιολική Ενέργεια και Ανεμογεννήτριες.
Patel, M. R. Wind and Solar Power Systems, ISBN: 0-8493-1570-0.
Messenger, R. A. and Ventre, J. Photovoltaic Systems Engineering, ISBN: 0-8493-1793-2.

Assessment

Progress assessments	35%
Laboratory assessments	20%
Final exam (written)	45%

Language

Greek

ΕΕΝ 445 Renewable Energy Sources

Purpose and Objectives: This course is designed to provide engineering students with an in-depth and critical understanding of renewable energy technologies, with a strong focus on photovoltaic and wind energy systems. The course aims to develop the ability to critically assess, design, and implement renewable energy systems, while addressing real-world engineering constraints and sustainability considerations. The main objectives are to: Deepen students' theoretical and practical understanding of key renewable energy technologies. Develop critical thinking and design capabilities related to the analysis and integration of renewable energy systems. Enable students to interpret and apply complex engineering principles for the development of efficient and sustainable energy solutions. Foster the ability to evaluate system performance, considering both technical and environmental factors. Expose students to modern energy storage technologies and hybrid system configurations.

Learning Outcomes: Upon successful completion of the course, students will be able to: 1. Acquire in-depth knowledge of the analysis, design, and implementation of photovoltaic and wind turbine systems. 2. Critically assess the operation, performance, and integration of PV and wind energy systems. 3. Develop technical specifications and conduct system-level assessments under varying conditions. 4. Design and optimize renewable energy systems using advanced engineering methods. 5. Use simulation and design tools to model and analyse system behaviours. 6. Evaluate and improve the performance of PV and wind systems. 7. Compare and select appropriate energy storage solutions for integration with renewable systems. 8. Propose innovative solutions to enhance the functionality and resilience of renewable energy systems, in line with emerging trends.

Course Content: Photovoltaic Power Systems, The photovoltaic effect and electrical energy conversion principles. PV panels: construction, electrical characteristics, and array configurations. Design and implementation of photovoltaic arrays. Electronic converters and their role in PV systems. Maximum Power Point Tracking (MPPT) techniques. Wind Power Systems, Wind energy fundamentals and wind turbine types. Power extraction principles and efficiency considerations. Converter systems for wind turbines. MPPT strategies for wind power applications. Electrical machines for wind applications Other Renewable Energy Technologies and Energy Storage, Overview of additional renewable technologies (e.g., small hydroelectric systems). Battery technologies and other electric storage systems.

BSc in Computer Science and Engineering

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