Course title

Advanced Topics in Software Engineering

Course code

CSE 521

Course type

Compulsory

Level

Master

Year / Semester

First Year / A Semester

Teacher’s name

Andreas Andreou

ECTS

7

Lectures / week

3

Laboratories / week

-

Course purpose and objectives

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

1. Software Engineering for Distributed Systems and the Cloud
2. Process Mining
3. Software Reuse

Learning outcomes

(a) summarize the key principles, problems and challenges in software development for distributed systems and cloud computing;

(b) consider the DevOps approach and evaluate the benefits of using it;

(c) familiarize, develop and use service software (online and offline);

(d) introduce and explain distributed transaction processing and software systems that use the Blockchain

(e) introduce the main concepts and principles of process mining with real-world examples;

(f) identify software development through reuse, and in particular scalable systems with software components

Prerequisites

Undergraduate 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:

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

Complementary:

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

Research Articles.

Assessment

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

Course title

Machine Learning Systems at Scale

Course code

CSE 522

Course type

Compulsory

Level

Master

Year / Semester

First Year / A Semester

Teacher’s name

Michael Sirivianos

ECTS

8

Lectures / week

4

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.

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. Topics covering the lifecycle of data analysis for ML, including data sourcing and preparation for ML, ML programming models, scalable model creation systems, and emerging topics such as ML system governance, explainability, and ethics (fairness, transparency, etc.).

Learning outcomes

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

1. Describe the major design principles of distributed systems.
2. Understand and implement key techniques for large-scale distributed systems.
3. Comprehend the functioning of cloud computing and ML infrastructure from companies like Google, Amazon, and Facebook.
4. Read and understand research papers on computer systems engineering.
5. Implement and modify complex large-scale distributed systems for Machine Learning and evaluate their performance.

Prerequisites

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

Required

-

Course content

Week 1: Process Management. The Unix Time Sharing System

Week 2: Memory Management

Week 3: File Systems. Virtual Machines: LFS, Xen

Week 4: Infrastructures for Big Data (Google): Google Filesystem, Google BigTable, Google Borg

Week 5: ML Lifecycle Overview

Week 6: Classical ML Training at Scale: Parameter Server, XGBoost

Weeks 7-8: Deep Learning Systems: TensorFlow, TVM

Weeks 9-10: System-assisted Feature Engineering and Model Selection: Cerebro, MSMS

Week 11: Data Sourcing and Preparation: TFDV, Snorkel

Weeks 12-13: ML Deployment; Clipper Online Prediction Serving System, Federated Learning

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. Discussions that promote reflection and critical thought, by assigning students to write two-page summary papers.

Bibliography

• Main Reading Materials:
  • Research articles which are available on Moodle.

• Optional:
  • Operating Systems Concepts (book) by Silberschatz, Galvin, and Gagne

Book slides at http://codex.cs.yale.edu/avi/os-book/OS9/slide-dir/

• Data Systems for Machine Learning lectures by Arun Kumar

         https://cseweb.ucsd.edu/classes/wi24/cse234-a/schedule.html

Assessment

• 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.
• Research Paper Presentations: Groups of two students will prepare a 60-minute presentation on the discussed paper. Presentations should be thorough (30-40 slides) and encourage questions and discussion. 
• Final Exam: Closed-book exam on all taught material with internet access only to download the research articles and related notes. Generative AI in any form (including more advanced search tools like Perplexity) are prohibited. The exam answers must be handwritten.

Note: Graduate students who have already taken the undergraduate version in a previous year must present two research articles in groups of two. Each presentation will account for 20% of the total grade. Additionally, they must submit and present, using 10 slides, a semester project involving the use, modification, and evaluation of a large-scale computational framework covered in the course. The semester project will constitute 60% of the total grade.

Course title

Deep Learning I

Course code

CSE 523

Course type

Compulsory

Level

Master

Year / Semester

First Year / A Semester

Teacher’s name

Sotirios Chatzis

ECTS

8

Lectures / week

4

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 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.

Learning outcomes

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

1. Describe and classify the fundamental design principles of deep learning systems and neural network architectures.
2. Implement and use widely accepted techniques for constructing and training deep neural networks (including parameter tuning and basic regularization).
3. Summarize and critically evaluate research articles on core deep learning topics.
4. 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

Yes

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, contrast with traditional feature engineering.
• 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.
• Optimization and Tuning Methods: Hyperparameter tuning (learning rates, batch size, etc.), initialization strategies, and normalization techniques such as batch normalization to speed up training.
• Convolutional Neural Networks (CNNs): Principles of convolutional layers and pooling, classic CNN architectures for image recognition, and understanding how CNNs extract spatial features.
• Recurrent Neural Networks (RNNs): Sequence modeling with RNNs, vanishing/exploding gradient issues, and an introduction to gated units (LSTM/GRU) for handling long-term dependencies.

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 before each class 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. (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).

Assessment

Course title

Network Science

Course code

CSE 524

Course type

Compulsory

Level

Master

Year / Semester

First Year / A Semester

Teacher’s name

Fragkiskos Papadopoulos

ECTS

7

Lectures / week

3

Laboratories / week

-

Course purpose and objectives

This course focuses on the emerging science of complex networks and their applications. The aim of the course is to teach students the mathematical theory of complex networks so that the students can apply this theory to solve relevant practical problems in the fields of technology, sociology, and biology, as well as in solving complex network research problems (Internet, Online Social networks, Transportation networks, Brain networks, metabolic reaction network problems, etc.).

Learning outcomes

Graduate students will gain knowledge of the properties of real-world complex networks and network analysis techniques, as well as knowledge of ongoing network science research. Specifically, students are expected to gain knowledge in topics such as graph theory, random networks, small-world and scale-free networks, the Barabási-Albert model, network geometry, spreading phenomena and their analysis, statistical inference techniques for predicting network behavior, the Gephi tool for network visualization, etc. Students will be able to apply this knowledge to the analysis and design of real-world networked systems.

Prerequisites

Probabilities and statistics, algebra.

Required

-

Course content

Graph theory, random networks, small world networks, scale-free networks, Barabási-Albert model, network geometry, network embeddings, hyperbolic geometric graphs, network navigation, statistical inference, prediction, multiplex networks, temporal networks, spreading phenomena.

Teaching methodology

Weekly Lectures. Each week the course instructor will present the topic in the form of a lecture presentation and group discussions will follow. The course will be a blend of presentations, problem solving activities, and group discussions.

Bibliography

Network Science, A.-L. Barabási, Cambridge University Press; 1 edition (August 5, 2016).

Assessment

Course title

Data Science

Course code

CSE 525

Course type

Compulsory

Level

Master

Year / Semester

First Year / B Semester

Teacher’s name

Simos Gerasimou

ECTS

8

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 realization 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: 

1. Distinguish between different types of data that are generated in science, engineering and design, and employ strategies for ensuring data quality.
2. Retrieve data from a variety of different data sources in a variety of different formats.
3. Apply inferential statistics and statistical procedures to test hypotheses about features and relationships within data sets.
4. Use appropriate visualisations to present and explore data sets.
5. Use databases, both relational and of other paradigms, to store and query data.
6. Implement supervised and unsupervised machine learning algorithms.             
7. 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 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.
• VanderPlas, J. Python Data Science Handbook: Essential Tools for Working with Data, O’Reilly, 2016.
• Igual, L. Segui, S. Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications, Springer, 2017

Assessment

Course title

Scalable Data Processing Systems

Course code

CSE 526

Course type

Compulsory

Level

Master

Year / Semester

First Year / B Semester

Teacher’s name

Herodotos Herodotou

ECTS

7

Lectures / week

3

Laboratories / week

-

Course purpose and objectives

Learning and understanding concepts, techniques, and systems related to scalable data processing using parallel relational database systems, NoSQL systems, and dataflow systems such as MapReduce and Spark.

Learning outcomes

Students will learn about the functionality of modern systems and techniques for large-scale data management processing. Students will be able to select and utilize the appropriate tools depending on the type of analysis they want to perform and the structure of the data they want to process.

Prerequisites

CSE 325 - Database Systems

Required

-

Course content

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 distributed processing systems such as MapReduce and Spark. 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 and NoSQL databases (column, document, and key-value stores). The course material will be drawn from textbooks as well as recent research literature.

Teaching methodology

Lectures, group discussions, lab exercises, and semester project.

Bibliography

• Abraham Silberschatz, Henry F. Korth, S. Sudarshan. Database System Concepts. 7th Edition, McGraw-Hill, 2019, ISBN: 9780078022159
• Recent research literature

Assessment

Course title

Research Methods in Computer Science and Engineering

Course code

CSE 527

Course type

Compulsory

Level

Master

Year / Semester

First Year / B Semester

Teacher’s name

* Will be taught by Special Scientist

ECTS

7

Lectures / week

3

Laboratories / week

-

Course purpose and objectives

The purpose of the course is to equip students with the knowledge and skills necessary to conduct research in a structured and methodologically sound way. Students will explore the nature and role of research in technological development, innovation, education, and the UN Sustainable Development Goals (SDGs), and how research can be effectively applied in these contexts. They will learn to search for and critically assess scientific information using digital tools and online databases, while observing principles of academic integrity and intellectual property.

The course will also familiarize students with the evaluation of scientific publications and the use of research impact metrics. It aims to prepare students to conduct research at an internationally competitive level, with attention to risk and project management, financial oversight, and effective communication of results through reports and scientific publications. Students will also gain experience in preparing research proposals and writing scientific articles for conferences, journals, and workshops, as well as in the development of high-quality master’s or doctoral theses.

Other key topics include intellectual property rights (IPR), data protection (GDPR), and research diplomacy (Science/Cultural Diplomacy). Emphasis will be placed on understanding how science and cultural heritage can support international collaboration and policy development. Students will be introduced to the concept of scientific and cultural diplomacy as a tool for advancing research agendas, institutional objectives, and national or international interests. Where possible, students may attend guest lectures or seminars delivered by academic staff or invited experts.

Learning outcomes

By the end of the course, students will be able to demonstrate the following:
Knowledge and Understanding

1. Understand the different types of research and the appropriate methods for conducting them.
2. Search for and retrieve information from libraries and online databases.
3. Write scientific papers for conferences, journals, and research proposals, including: 
   ·       Critical review of scientific literature 
   ·       Evaluation of scientific work 
   ·       Research proposal writing 
   ·       Literature review writing
4. Prepare scientific theses at postgraduate level for both open and restricted access formats.
5. Design and carry out research tasks and experiments relevant to their field.
6. Understand data collection, storage, preservation, and processing practices, with awareness of intellectual property rights (IPR) and data protection regulations (GDPR).
7. Recognize principles of risk and time management in the planning and execution of research projects.
8. Understand the concept and significance of scientific and cultural diplomacy.

Skills and Competencies

1. Efficiently locate and manage academic sources using physical and digital library tools and online databases.
2. Study a research topic in depth and produce a well-structured literature review.
3. Collect, handle, and process data in accordance with legal and ethical standards.
4. Write competitive research proposals and high-quality scientific articles for academic publication.
5. Produce academically sound and well-documented postgraduate theses.
6. Apply principles of risk and time management in research settings.

Prerequisites

-

Required

-

Course content

The course is structured around five modules, each addressing key aspects of research practice and methodology in computer and informatics engineering. The modules are organized to guide students progressively from foundational concepts to the effective communication of research work.

·Module 1: Research Basics

• Research Methods in Computer Science and Engineering
• Empirical Methodology for Conducting Research
• Formal Methodology for Conducting Research
• Research Supervision

·Module 2: Library Tools and Literature Search

• Library Tools
• Library Search
• Database Search Tips
• Digital Libraries

·Module 3: Literature Review

• Types of Scientific Literature
• Analyzing a Scientific Paper
• Writing a Paper Review
• Systematic Literature Reviews

·Module 4: Data

• The Nature of Data
• Collecting Primary Data
• Collecting and Analyzing Secondary Data
• Quantitative Data Analysis
• Qualitative Data Analysis

·Module 5: Writing Up Your Work

• Writing a Scientific Research Paper
• Writing a Thesis

Teaching methodology

The course will be delivered through a combination of lectures and supervision sessions with academic advisors. Lectures will cover the core topics defined in the course modules, providing both theoretical foundations and practical guidance on research methodology, scientific writing, data handling, and time management.

In addition to these sessions, students will attend presentations by library personel, who will demonstrate the use of digital bibliographic tools and academic databases for conducting literature searches and accessing scientific information. This will help students develop effective information retrieval skills using modern research infrastructure and technologies. Where feasible, students will also have the opportunity to attend seminars given by invited speakers or academic personnel from other units of the university. These sessions aim to broaden students’ understanding of research practices and highlight interdisciplinary perspectives.

Bibliography

• Primary Resources

• Laurel, B. (Ed.). Design Research: Methods and Perspectives. MIT Press, 2003. ISBN: 978-0262122634
• Bell, J., Waters, S. Doing Your Research Project: A Guide for First-Time Researchers. McGraw-Hill Education (UK), 2014.
• O’Leary, Z. The Essential Guide to Doing Your Research Project. 3rd ed., SAGE Publications, 2021.
• Instructor-prepared lecture slides and notes, distributed weekly.

• Supplementary Materials (Optional)

• Cohen, L., Manion, L., Morrison, K. Research Methods in Education. 7th ed., Routledge, 2011.

Assessment

Course title

Deep Learning II

Course code

CSE 528

Course type

Compulsory

Level

Master

Year / Semester

First Year / B Semester

Teacher’s name

Sotirios Chatzis

ECTS

8

Lectures / week

4

Laboratories / week

-

Course purpose and objectives

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.

Learning outcomes

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

1. Describe advanced deep learning architectures (including transformers and other novel network structures) and explain the principles behind their design and functionality.
2. Apply and implement state-of-the-art deep learning techniques, such as generative models and advanced regularization methods, to solve complex problems.
3. Critically evaluate modern deep learning research publications, and effectively present and discuss the findings of advanced research articles in the field.
4. Develop and adapt deep learning solutions for large-scale or cutting-edge applications, demonstrating the ability to handle the practical challenges of training and deploying complex models.

Prerequisites

Deep Learning I (or equivalent foundational course in deep neural networks), or a solid understanding of neural network basics, training methods, and CNN/RNN architectures.

Required

Yes

Course content

• Transformer Models and Self-Attention: In-depth study of transformer architecture and self-attention mechanism; transformer-based models for sequence learning (e.g., the "Attention is All You Need" framework, BERT/GPT family models).
• Generative Deep Networks: Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs); principles of deep generative modeling and examples of how these models synthesize data.
• Diffusion Models: Introduction to diffusion-based generative models and related approaches for high-quality data generation (e.g., Denoising Diffusion Probabilistic Models and their applications in image/audio synthesis).
• Advanced Regularization Techniques: Cutting-edge methods to improve generalization and robustness in deep learning (beyond basics) – for example, data augmentation strategies, adversarial training for robustness, and other recent regularization approaches suited for large models.
• State-of-the-Art Applications: Exploration of current frontiers and applications of deep learning, such as large language models in NLP, advanced computer vision systems, deep learning in reinforcement learning and other domains. Discussion of ethical considerations and interpretability as relevant to these cutting-edge applications.

Teaching methodology

As with Deep Learning 1, this course is delivered through weekly lectures and interactive sessions. In approximately the first seven weeks, the instructor presents advanced deep learning topics through lectures. In the final six weeks, students take the lead by presenting selected recent research papers that correspond to the advanced topics covered.

The instructor provides detailed slides, notes, and references for each lecture, and regularly poses oral questions about both the lecture content and the research articles to ensure active student engagement. Students are expected to write and submit short summaries of the assigned research papers before classes and to actively participate in discussions during class.

This format, combining instructor-led lectures with student presentations and Q&A discussions, encourages deeper inquiry into the material and helps students develop critical analysis skills and confidence in discussing current research in deep learning.

Bibliography

• Primary Materials:

• A collection of up-to-date research articles (made available on the course’s Moodle page) constitutes the core reading for each topic. These include seminal papers on transformers, diffusion models, and other advanced techniques, serving as the primary source of knowledge for the course.

• Textbook and Supplementary Reading:
  • While no single textbook covers all the emerging topics, students may refer to Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (MIT Press, 2016) for foundational chapters relevant to some topics.
  • Additional resources, such as recent survey papers and online tutorials/documentation, will be recommended by the instructor for topics like transformers and diffusion models to complement the lectures and paper readings.

Assessment

Course title

Security and Privacy in AI & Data Systems

Course code

CSE 529

Course type

Compulsory

Level

Master

Year / Semester

First Year / B Semester

Teacher’s name

Panagiotis Ilia

ECTS

7

Lectures / week

3

Laboratories / week

-

Course purpose and objectives

This course focuses on the security and privacy aspects encountered during the design, implementation, and deployment of intelligent and data systems. It aims to provide students with a clear understanding of how security and privacy risks arise within AI/ML and data-driven environments, and how these risks can be addressed through appropriate architectural and design strategies. Students will develop the ability to identify and assess vulnerabilities, model potential threats, and reason critically about privacy-conscious and security-informed design decisions, with particular emphasis on risks such as data leakage, model inversion, and adversarial manipulation.

The course builds on foundational concepts such as data integrity, authentication, access control, and threat modeling, applying them to the analysis and design of trustworthy data and AI systems. It examines privacy-enhancing methods including anonymisation, differential privacy, and federated learning, with attention to their assumptions, limitations, and design trade-offs. Security and privacy concerns are addressed across multiple layers of modern infrastructures, including data pipelines, web-based platforms, and AI/ML workflows. Topics such as tracking, fingerprinting, data poisoning, membership inference, and client-side vulnerabilities are discussed, alongside system-level principles like defense-in-depth and input sanitization. The course also introduces secure multiparty computation as a conceptual extension of privacy-by-design, and considers how architectural decisions impact system resilience, transparency, and alignment with privacy expectations and regulatory frameworks such as the GDPR.

Learning outcomes

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

1. Explain the core principles of security and privacy, including confidentiality, integrity, availability, authentication, access control, and the distinction between privacy and security.
2. Identify and analyze vulnerabilities and threats, applying threat modeling to assess risks across different types of architectures and system contexts.
3. Evaluate privacy-preserving approaches, including anonymisation, pseudonymisation, differential privacy, federated learning, secure multiparty computation, as well as understanding their assumptions, limitations, and applicability to system design.
4. Assess security and privacy risks in intelligent systems, including adversarial attacks, data poisoning, membership inference, and model inversion.
5. Analyze security, privacy and data exposure risks in web-based infrastructures, including tracking, fingerprinting, and client-side vulnerabilities.
6. Apply system-level design principles, such as defense in depth, sanitization, and privacy by design, to develop secure and privacy-aware architectures and reason about trade-offs in performance, resilience, fairness, and transparency.

Prerequisites

CSE 423 - Introduction to Cryptography and Computer Security

Required

-

Course content

The course content is organized into five core modules, each covering a distinct set of technical areas and concepts.

• Module 1: Core Concepts in Security and Privacy
  Confidentiality, integrity, availability (CIA); authentication, authorisation, and access control; privacy vs. security; attacker models; introduction to threat modeling using STRIDE and LINDDUN.
• Module 2: Data Protection Techniques and Limitations
  Anonymisation, pseudonymisation, and re-identification risks; differential privacy and federated learning; secure multiparty computation and homomorphic encryption as conceptual models.
• Module 3: Security and Privacy in Intelligent Systems
  Adversarial attacks, data poisoning, and backdoors; membership inference and model inversion; mitigation strategies; privacy and explainability trade-offs in AI/ML systems.
• Module 4: Web-Based Privacy and Exposure Risks
  Data collection on the web; browser security models (Same-Origin Policy, CSP, CORS); tracking, fingerprinting, and client-side vulnerabilities; implications for system design.
• Module 5: Secure System Design and Trade-offs
  Defense in depth, input sanitization, data minimization, security and privacy by design; logging and retention policies; system resilience, fairness, transparency, and design alignment with GDPR and ethical considerations.

Teaching methodology

The course will be delivered through weekly lectures given by the instructor, using material specifically prepared for this course along with selected complementary resources. Lectures will cover the topics outlined in the five course modules, introducing students to relevant concepts, techniques, and design considerations. Each lecture will also include guided discussions and questions designed to encourage active participation and deepen students’ understanding of the presented concepts. After each lecture, students will be expected to prepare a short written summary of the material covered. A short discussion based on these summaries will take place at the beginning of the following lecture, to reinforce understanding and provide an opportunity for clarification and reflection.

In the final two weeks of the course, students will select and critically review a research paper from a curated list of papers related to the course topics. Each student will present their chosen paper and lead a short discussion session, with emphasis on identifying key contributions, limitations, and connections to the topics covered throughout the course. This activity will introduce students to current research work and help them build skills in analysis, critical reflection, and technical communication, while also encouraging them to draw connections across topics, assess the relevance of academic work to real-world challenges, and articulate informed perspectives on emerging issues in the field.

Bibliography

• Primary Materials

• Instructor-prepared lecture slides and notes, provided weekly in alignment with the course modules. These materials form the primary reference for lecture content and are intended to support students in preparing their written lecture summaries and in-class discussions.

• Textbooks and Supplementary Materials

No single textbook covers all topics addressed in this course. Students are expected to consult selected chapters from the following recommended books, as relevant to each module:

• Stallings, W., Brown, L., Computer Security: Principles and Practice. 4th ed., Pearson, 2017.
• Hoffman, A. Web Application Security: Exploitation and Countermeasures for Modern Web Applications. 1st ed., O’Reilly Media, 2020.
• Chio, C., Freeman, D. Machine Learning and Security: Protecting Systems with Data and Algorithms. 1st ed., O’Reilly Media, 2018.

• Research Papers:

• A curated list of research papers will be provided, covering core topics and recent developments in the field. These include work on adversarial machine learning, privacy-preserving techniques, and web security vulnerabilities among others. Students will be required to critically review and present one paper during the final 2 weeks of the course, linking its content to the topics and concepts covered throughout the semester.

Assessment