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.

Data and computer communication fundamentals. Protocols and local and wide area networks. Introduction to Internet and networking tchnologies. TCP/IP suite of protocols, Quality of Service (QoS). New networking architectures. Protocols and standards (e.g. Diffserv, ipv6, MPLS). Network and protocol performance evaluation (queuing theory, and simulation tools). Traffic modeling and Engineering. Congestion control and resource allocation. Network design and optimisation.

Ohm’s Law, KCL, KVL, Voltage and current divider, Electric Power.Operation and construction of Diodes, Bipolar Transistor, Field Effect Transistors (JFET, OSFET) new technologies. Biasing, stability of biasing, switching characteristics, delays leading to Hazards. Logic Gates circuits, transfer characteristics of ideal and real gates, input – output voltage levels. Families of logic gates. Operational Amplifiers, comparators and mathematical operators. Analogue to digital (ADC) and Digital to Analogue Converters (DAC), Data Acquisition System.

Introduction to CMOS. Boolean algebra, its axioms and rules, prime implicants, principle of duality, simplification and minimization using Karnaugh maps, De Morgan’s Laws. Binary mathematical systems. Timing analysis and worst path determination, timing hazards in logic circuits. Networks of logic gates, combinational and sequential synthesis. Finite state machines (FSMs), state tables, state assignment and minimizations and their use in sequential and control circuits. Synchronous and asynchronous circuits, signaling and handshaking. Adders, subtractors, multipliers, dividers, the ALU. Encoders and decoders. Counters, shifters and shift-registers. Multiplexors and de-multiplexors. Registers and register files. SRAM and DRAM cells. Error-detecting codes. Sequential and combinational design with CPLDs, GALs, FPGAs, PLAs. Introduction to hardware-description languages (VHDL or Verilog). Memory structures such as RAM, ROM. The course includes laboratory experiments involving the design and testing of digital systems using small and medium scale integrated circuits.

Logic, algebra of propositions, predicate logic. Methods of proof. Sequences and summations. Principles of induction and rules of inference. Set theory. Relations and functions. Boole Algebra and applications. Combinatorics. Fundamental principles of algorithms. Graphs and trees.

This course covers the basic principles involved in the construction of compilers and programming languages. It begins with an introduction consisting of a review of the different phases involved in the compilation of a source program to a destination language. It then proceeds to each phase separately with emphasis placed on the topics of lexical, syntactic and semantic analysis as well as the production of intermediate code (three address code) in a syntax directed way. The ideas are demonstrated via the development of small lexers using flex and parsers using bison. The course is continuously reviewed and extended to cover more areas in greater depth.

The study of methodologies in the area of analysis and design of Information Systems. The phases of the System Development Life Cycle (SDLC) and the role of the analyst, users and management. Information gathering methods. Project management. Feasibility analysis. Modeling and Design of the Information System.

Τhis course is an introduction to Data Structures and the Analysis of Algorithms. It aims at the understanding of computational concepts, and of how computational problems can be solved efficiently by computers. This course enables students to: a) describe and implement in C/C++ basic data structures and algorithms, such as lists, stacks, queues, search trees, sorting algoritms and hashing; b) implement programming techniques that are often encountered in dealing with computational problems such as recursion, greedy programming and dynamic programming; c) to analyze the complexity of algorithms and assess the computational cost of their algorithms; d) to choose correctly among algorithms and data structures for the most appropriate solution of a computational problem; e) to combine algorithms and data structures for the solution of complex computational problems.

The course is an introduction to operating systems and related types of systems software. The topics covered are: a) introduction to the history and evolution of Operating Systems; b) the structure and features of Operating Systems; c) process management (creation, synchronization, and communication); d) concurrency mechanisms and algorithms, (mutual exclusion, scheduling and deadlocks); e) main and virtual memory management (swapping, paging, segmentation and page-replacement algorithms); f) disks and other input/output (I/O) devices; g) file-system structure and implementation; h) security and reliability.

The course focuses on the design and analysis of efficient algorithms and their complexity. In particular, the course covers various topics including algorithm analysis, asymptotic analysis, backtracking, divide-and-conquer algorithms, dynamic programming, greedy algorithms, graph representation, graph search, minimum spanning trees, shortest paths, maximum flow, NP completeness, and approximation algorithms. Prerequisite background: Data Structures.

Introduction to the Unified Modeling Language (UML): Class and object modeling, class diagrams, interactions diagrams (sequence, collaboration), use cases, activity diagrams, state modeling. Advanced object-oriented programming principles covering object-oriented data structures and their associated algorithms, such as lazy evaluation. Implementation of heaps, self-balancing trees, graphs, sorting, and generative recursion via objects. Applications of software engineering principles such as abstract decomposition, decoupling, and command passing to large- and small- scale component-framework systems. Multi-threaded event-driven applications. Prerequisite: CEI 123 - Object Oriented Methodology and Programming (C++).

Prerequisites

EEI 133 Object-oriented Methodology and Programming with C++

The course provides a deep understanding of concepts and practices of systems programming based on the Unix OS. File I/O, system calls and buffering. Assemblers, loaders, linkers, preprocessors, parsers and stack frames. Memory management, system processes (fork, exec, dup, pipe), system libraries, sockets, client-server programming, threads. Alternative OS (Linux, Android). Introduction to embedded systems. 

The course provides the opportunity to students to interconnect LANs in the laboratory using switches, routers, terminal servers etc., and conduct various experiments relating to the TCP/IP protocol stack. Students perform hands-on experiments in subnetting, DHCP, DNS, TCP handshaking and congestion mechanism, wireless protocols (WiFi), SSL, SIP-VoIP, router based firewall, RIP, OSPF and multicast routing. In the process, students learn to use network software tools, to perform network troubleshooting and configure network devices. The course covers basic practical aspects on network programming, the OSI model, the TCP and UDP protocols, ports and IP addressing (classes, addresses, subnet masks, networks, sub-networks, end-users), network devices (routers, switches, hubs, bridges), structured cabling, sockets and socket programming (commands socket, connect, bind, listen, accept, fork, exec, write, read) and the client-server model through sockets.

Presentation of advanced uni-processor architectures and parallel architectures such as multi-processors and multi-cores. Advanced pipelining; dynamic scheduling, Scoreboarding and Tomasulo’s algorithms, out-of-order execution, precise interrupt handling. Multiple instruction issue in superscalar processors. Data, control and structural hazards minimization. Branch predictors and branch history tables. Techniques for exploiting sub-word parallelism, instruction-level parallelism and thread-level parallelism. Static parallel instruction scheduling in VLIW architectures, loop unrolling and software pipelining. Advanced memory system organization, interleaved memory, multiple cache architectures, virtual memory, TLBs. Storage systems, and reliability with RAID storage systems. Cache coherence protocols in multi-processors and multi-core architectures. Basic networks and interconnection networks, on- and off-chip. The course may include a hands-on project using academic or industrial architecture simulators.

Prerequisites: 

EEI 233 Computer Organization

Methods, tools, and procedures for the development and maintenance of large-scale software systems within specified quality and cost constraints. Life-cycle models, specification techniques, software development methodologies, verification and validation, CASE, and other tools. The Object-Oriented Paradigm and UML. Project planning and management. Practical experience with CASE tools for modeling data and functions. Prerequisite: CEI 223 - Information Systems Analysis and Design

The course gives a solid background in databases with a focus on relational database management systems. Topics include data modeling, database design theory and methodology, data definition and manipulation languages, storage and indexing techniques, query processing and optimization, transactions, concurrency control and recovery. The course also covers fundamentals of database management system architecture and techniques for database application development.

Τhis course deals with Web Information technologies, which have nowadays become the main software application platform. We will cover a wide range of frameworks for the development of WWW applications, which are used in critical large scale infrastructures, such as Online Social Networks, Banking, Online Stores, Online Video Services etc. We will describe the basic design principles of Internet applications that span protocols, standards, data management, multi-tier architectures and security. We will study how we can design and implement reliable, user-friendly, scalable, flexible, secure and maintainable applications. In addition, we will study several academic articles that describe the design philosophy of systems on which the WWW infrastructure relies upon. At the core of the course, we have a series of Web system implementation exercises, which equip the students with the programming and infrastructural knowledge needed to create an advanced web application. Course outcomes: a) students will acquire knowledge pertaining the basic design principles of Internet applications, which span protocols, standards, data management, multi-tier architectures and security; b) students will learn how to design and implement reliable, user-friendly, scalable, flexible, secure and maintainable web applications; c) students will study several academic articles that describe the design philosophy of systems on which the WWW infrastructure relies upon; d) through a series of Web system implementation exercises, the students will acquire programming and infrastructural knowledge needed to create an advanced web application.

The course is an introduction to algorithms, design, hardware, and software of modern distributed systems, emphasizing both efficiency as well as robustness and security. Topics covered include distributed system architectures, processes, inter-process communication, naming in distributed entities, coordination, and distributed consensus in fault-prone environments.

Learning Outcomes:

1. Students will acquire knowledge related to fundamental distributed system design and techniques for achieving essential functionalities such as distributed consensus.
2. They will gain deep technical understanding regarding when and how to leverage the capabilities of modern parallel and distributed systems to meet organizational needs.
3. They will be trained in critically understanding controversial issues to distinguish genuinely scientific information from marketing hype. For instance, why do people claim that centralized solutions are something we never want? Ultimately, how useful is blockchain technology?

Students are divided up into small groups to develop (analyze, specify, design, program, and document) a real-world software application with a client organization coming from the local market. The aim of the course is to gain experience in the various phases of software development and in different aspects of group working. Special attention is given to Project Management activities, such as scheduling, time and resource management, production of reports and deliverables, etc. The group develops abilities to "pick up" and use new development tools and environments without formal training. The final product is a fully operational software system which is installed at the client site with execution of professional user acceptance testing and appropriate training.

Prerequisite:

CSE 324 - Software Engineering

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 (feature extraction) technologies, namely Deep Learning methods. On this basis, we go on to present a fundamental Deep Learning architecture, namely Convolutional Neural Networks, as well as some recent extensions of it. Further, we introduce basic supervised learning models, including linear regression, logistic regression, linear discriminant analysis (LDA), and Large Margin Machines. Regarding the latter, we put some emphasis on the theory of Reproducing Kernel Hilbert Spaces, and Matern Class Kernels. 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. We conclude with the presentation of the design, deployment, and evaluation of a real-life AI system, used in a commercial application (Google’s autonomous vehicles).

This course is an introduction to Cryptography and Computer Security. Emphasis will be given on understanding the basic principles of computer security and cryptography, specifically aiming to teach students on how to design and implement secure systems. This course covers the following topics: Fundamentals of computer security and networks security, objectives and basic principles of cryptography. Vulnerabilities, threats and attacks. Principles of designing secure computing systems. Privacy and Anonymity. In particular, the course provides an introduction to Cryptography, Pseudo Random Functions, Hash functions, Message Authentication Codes (ΜΑCs) and Digital signatures. Digital certificates and Public Key Infrastructures (PKI), certificate transparency and certificate revocation. Symmetric-key cryptography and cryptographic algorithms (Block Ciphers, Stream Ciphers). Asymmetric-key cryptography, Elliptic curves cryptography. Zero-Knowledge Proofs. Key generation and key exchange techniques and cryptographic protocols. Furthermore, this course will introduce the basic categories of malware, such as Viruses, Worms, Trojan horses, Backdoors, Rootkits as well as a view into botnet architectures. Methods of infection and replication, polymorphism and stealth techniques. Antiviruses and detection techniques. The course also introduces the most common types of attacks, such as Buffer Overflows, SQL Injection, Cross-site scripting, Denial of Service attacks, Social engineering and Phishing attacks, ClickJacking etc. Finally, we will explore detection and prevention techniques, as well as protection mechanisms.

This course delves into and the design and implementation principles of distributed and networked operating systems. We emphasize classic operating systems design principles and the description of modern systems that form the basis for the Internet and the World Wide Web infrastructure. The course syllabus includes the following. End-to-end design principle and the philosophy of the internet architecture. Upcalls. Remote-procedure calls. Log-structured file systems. Distributed file systems. Microkernels. Virtualization. Distributed Hash Tables. Large-scale key-value stores. Map-Reduce, Pregel and Spark. Time, clocks and the ordering of events. Distributed consensus with Paxos. Large scale synchronization (ZooKeeper). Distributed transactions and notifications. Content distribution networks). Software-defined-networking. At the end of the course, the students will be able to: a) describe the fundamental design principles of distributed systems; b) to describe and use widely-accepted techniques for large scale system implementation; c) to understand the operational principles of the computing infrastructure of Google, Yahoo, Amazon, Facebook and other large scale online services; d) to read and understand research articles on computing systems engineering; e) to understand the implementation of a complex distributed system, so that they can evaluate its performance and modify it. This is a graduate level course and undergraduate students need the instructor's approval for enrollment. The workload for undergraduates differs from that of the graduate students mainly in that they are not required to submit a semester project.

This course aims at investigating advanced topics in the area of Software Engineering with emphasis on three main axes: (a) Software Testing, (b) Software Reusability, and, (c) Software Project Management. Each axis covers in depth multiple processes and methodologies required in practice to tackle problems of software quality in a fast changing environment comprising development tools, models and human resources. Testing will be involved with validating and verifying a software product using control and dataflow information, symbolic execution and formal models. Reusability will be examined as one of the most promising alternatives in software development anticipating to cut down costs and increase quality. Finally, selected software project management issues will be presented, both from the process improvement and from the human resources perspective. The students will also undertake in groups a large project that will complement their knowledge on the testing axis through the development of CASE tools for performing static code analysis and automated dynamic testing. Prerequisite: ΜΗΥΠ 324 - Software Engineering

The course aims to provide an introduction to the basic principles of Graphics and provide all the knowledge needed to create and visualize 2D and 3D virtual worlds, starting with simple basic principles of Geometry.

Students shall be able to integrate the following knowledge:

Basic concepts of 2D and 3D space and geometrical elements (points, lines, curves, triangles and polygon triangulation, planets - including complex polygons, freeform surfaces), Neighborhood of points, polygons: Voronoi diagram and basics. Delaunay triangulation and basic properties, incremental algorithm. Complex point clouds (from object digitization) and modeling. Geometric shapes and object modeling structures. Platonic solid bodies and 3D object reconstruction and reproduction, Computer Aided Design / Computer Aided Manufacturing - CAD / CAM. Collapse data and archive it. Simple examples in various areas and applications with programming.

Graphic and geometrical problems in different areas of life give us a good example of the variety of applications: Architecture, Archeology, Medicine, Engineering, Robotics, Simulations, Geographic Information Systems (GIS), Cinema, Video Production, etc.

Course Content

1. Basic and introductory concepts of graphics

2. Geometric elements of 2D and 3D space (points, lines, curves, triangles and polygon triangulation, planes)

3. Transformations and 2D viewing (PC screens)

4. Basic forms of object modeling structures

5. Simulation

6. Analysis and design of various applications and specialized examples

7. Planning and quality control

8. Graphics and application standards in industry

9. Computer Aided Design / Computer Aided Manufacturing - CAD / CAM.

The aim of this course is to teach advanced techniques in designing high-performance microprocessors and parallel and distributed computer systems. The major goal of this course is to carry out research work that will lead to the completion of a semester-long research project, whereby the students will enhance their knowledge and their skills in designing and simulating computer systems and processors and to perform quantitative performance analysis through the collected experimental results. The course will cover advanced topics in the fields of microprocessors and large-scale computing systems, such as multi-issue instruction pipelining, instruction-level parallelism, multithreaded architectures, very-large instruction word processors, sub-word parallelism, parallel computers, cache coherence protocols, distributed memory, distributed processors, parallel programming and on-chip networks. The course will cover broad themes pertaining to the state-of-the-art technology in the field of computer systems and high-performance micro-processors. As students gain knowledge in the first half of the course, they will then study several research papers during the second half of the course in the fields of parallel and distributed computing and microprocessors and will present the studied material during lecture periodically (either in pairs or individually). The gained knowledge will then help the students complete their research project that can be proposed by themselves (carried out with the approval by the instructor), or they will be given a research topic to be explored by the instructor. In either case, the conclusions drawn by the project will be derived from detailed simulations of computing systems that will be designed in such a way to encompass the tested research ideas. Prerequisites and background: The students should possess strong expertise in computer architecture and computer programming in languages such as C/C++. Additionally, they should possess basic knowledge in the field of digital circuit design. Also, preferably they should possess familiarity with basic computer network principles and mathematics particularly in the field of probability theory. This is a graduate-level course and undergraduate students need the approval of the instructor so as to register for this course. The expected workload of undergraduate students differs from that of postgraduate students as they are not expected to complete the aforementioned research project.

The need to store and process massive amounts of data has led to the evolution of existing database systems while a new breed of data processing systems has emerged. This course covers a spectrum of topics from core techniques in relational data management to highly-scalable data processing using parallel database systems and MapReduce. First, the course covers the basic principles in database query processing and optimization, including index structures, sort and join processing, query rewrites, and physical plan selection. Next, the course covers topics from parallel and distributed databases, including data partitioning and distributed join algorithms. Finally, the course covers scalable data processing systems such as MapReduce and NoSQL databases (column, document, and key-value stores). The course material will be drawn from textbooks as well as recent research literature. Prerequisite background: Basic database knowledge.

Prerequisites

CSE 325 Databases

The aim of the course is to introduce the basic principles of Computer Interaction Science (HSC) and its application in the design of computer systems interfaces. In particular, the importance of properly implementing HSC principles and how they affect the efficiency and effectiveness of computer systems interfaces is presented. Design methodologies and evaluation methods are explained to understand their role in design usability.

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

• Recognize capabilities and limitations in the design of computer interfaces for humans
• Understand the tools and techniques available to design interfaces for web and mobile platforms
• Apply Principles of Human Computer Interaction through design of computer systems interfaces.
• Evaluate existing interfaces against recognized principles of human computer design and interaction

Course Content

A Historical Review of the Science of Human Computer Interaction

Importance of proper computer system design

The human factor - memory, attention, communication models - capabilities and limitations

Input and output devices for human-computer interaction

Gestalt rules and their application to web and mobile interface design - use MockFlow to design low-fidelity prototypes

Usability components and its importance in evaluating interfaces

Low, intermediate and high-fidelity prototypes

LUCID design methodology

Evaluating interface design using Nielsen's heuristic rules

Evaluation methods: controlled experiments against field evaluation

Introduction to user personalization and modeling

This course introduces basic and advanced techniques for designing and testing integrated circuits. Several topics are covered concerning optimization of digital design in terms of various designs parameters as well as practical approaches in testing the designed Integrated Circuits. Specifically, topics covered in this course includes: Basic approach towards digital hardware design, Timing and optimizations, Pipelined Digital Design for high throughput, Parallel Digital Design for low area consumption, Parallel and Pipelined Digital Designs for low area consumption, Testbences, Challenges in 90nm Digital Hardware Design. 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, Parallel and Partial scan path design technique, Built-In Self-Test (BIST) techniques, LFSRs, Cellular automata, IEEE 1149.1 Boundary-scan.

Deep Learning 1 is the first part of a two-course sequence on deep learning, focusing on core concepts and techniques. This module covers the foundations of Deep Learning, enabling students to understand how to build and train neural networks effectively. Students will learn about fundamental neural network architectures, the principle of representation learning, training and optimization methods, and introductory knowledge of Convolutional and Recurrent Neural Networks. By the end of the course, students will be prepared with a solid base for more advanced deep learning topics in the subsequent module (Deep Learning 2) and for leading successful machine learning projects.

Deep Learning 2 is the second part of the deep learning series, concentrating on advanced topics and state-of-the-art techniques in the field. Building on the foundation from Deep Learning 1, this module introduces students to cutting-edge neural network architectures and methodologies. Students will explore modern developments such as transformer models and self-attention mechanisms, diffusion-based generative models, and other advanced generative architectures. The course also covers sophisticated regularization and optimization strategies, approaches for training large-scale models, and examines current state-of-the-art applications of deep learning in industry and research. The objective is to enable students to understand and apply these advanced techniques, preparing them to tackle contemporary research problems and to design innovative deep learning solutions.

Matrix algebra. Determinants. Inverse matrix and matrix equations. Solution of systems of linear equations. Vector algebra and applications. Linear spaces, linear independence, base, matrix rank and nullity. Linear transformations. Eigenvalues, eigenvectors and quadratic forms. Introduction to complex variables. Complex numbers, complex functions and calculus. Residues and contour integrals.

Introduction to basic computer organisation (microprocessors and memory structures) and computing (Turing machines, Finite State machines, regular languages, context free grammars). Introduction to modular programming, basic computer programming principles and software construction. Presentation of the C programming language concentrating on data types, control structures, loops, functions, matrices, strings, pointers and recursion. The course includes a number of programming exercises and a project.

This subject constitutes an introduction into object-oriented programming with C++. It commences with discussions of the basic principles involved in object –oriented programming like for example the definition of an object, a class, inheritance, input and output streams, pointers, types vs. classes, operator overloading. The following topics are covered in greater depth: Classes including derived classes, messaging. Classes about input/output streams. Abstract datatypes. Pointers. Inheritance (including virtual methods, static definitions, multiple inheritance). Operator Overloading. General abstract types. Knowledge of C is a necessary pre-requisite.

Σήματα Συνεχούς και Διακριτού Χρόνου σε Γραμμικούς Διανυσματικούς Χώρους, Κρουστικές Συναρτήσεις και Αποκρίσεις. Βασικές Ιδιότητες Γραμμικών Συστημάτων Αμετάβλητου Χρόνου (ΓΑΧ-LTI) για Συνεχή και Διακριτό Χρόνο, Διαφορικές συναρτήσεις σημάτων και συστημάτων. Συνέλιξη, Ανάλυση, Σύνθεση, και Σειρά Fourier για Περιοδικά και Απεριοδικά Σήματα Συνεχούς και Διακριτού Χρόνου. Μετασχηματισμοί Σειράς Fourier και Ζ για Σήματα Συνεχούς και Διακριτού Χρόνου. Πεπερασμένα και Άπειρα Σήματα και Συστήματα, Συνθήκη Σταθερότητας Σημάτων Συνεχούς και Διακριτού Χρόνου.

Course Goals

Introduce students to the basic concepts of Computer Organization.

Expected Results

Students shall have an in-depth knowledge of basic computer organization and structure, microprocessor structure and computer memory, as well as identifying methodologies for improving processor performance.

Content

The course covers fundamental operational structures of uni-processor computer systems and their basic building blocks: arithmetic and logical unit, control unit (FSM-based), memory system, I/O system, datapath. It also covers binary number systems, one’s and two’s complement arithmetic, floating point arithmetic, overflow and underflow, addition, subtraction, division and multiplication using binary numbers. Presentation of the RISC Central Processing Unit (CPU) using the MIPS architecture as a reference. RISC Instruction Set Architecture (ISA). Assembly programming in MIPS. Pipelining, data, control and structural hazards. Interrupt and exception handling. Random-Access Memory (RAM), cache memory organization (direct-mapped and set-associative), and virtual memory. CPU performance measurement and enhancements involving branch prediction and assembly instruction re-ordering. Storage systems such as hard disks and optical media. The course includes a weekly programming laboratory session for hands-on practice using the MIPS ISA assembly language.

This is a three-hour per week, 4-credit, required degree level course that concentrates on the learning of English for Academic Purposes (EAP). The course is particularly designed to support students’ studies by developing their English language to an academic level. This course aims to familiarise the students with reading material related to general and academic topics. This material is used to acquaint students with writing styles, such as narration and description. It also develops students’ listening skills. Furthermore, learners are expected to develop their speaking fluency by taking an active part in speaking tasks such as dialogues, conversations, and oral presentations.

This is a three hour per week, 4-credit, required level course that concentrates on the learning of English for Specific Academic Purposes. The course is particularly designed to meet the needs of university students studying in the field of Computer Engineering and Informatics. This course intends to familiarise the students with relevant reading material. This will be used to acquaint the students with writing styles, such as proposals, lab reports, process analysis, cause and effect. Furthermore, learners are expected to develop their listening comprehension and speaking fluency by taking an active part in discussions, giving oral presentations, defending their opinion etc. They are expected to develop sufficient range of language, phonological control and sociolinguistic awareness to be able to express themselves with a degree of clarity, fluency and spontaneity. Prerequisite: English for Academic Purposes