Computer Science

This course focuses on the fundamentals of artificial intelligence and understanding the implications of these techniques in creative processes and in society as a whole. It discusses the intersection of technology, creativity, and culture, and how AI can contribute to these areas. This course examines the potential of AI as a tool for artistic expression and creativity but also the ethical implications of AI, particularly in the context of art, culture and society.

This course introduces the fundamental concepts of problem solving by computing and programming using an imperative programming language. It is and introductory course to computing. Topics include computational thinking and computational problem solving, designing and specifying an algorithm, basic problem formulation and problem solving approaches, program development, coding, testing and debugging, fundamental programming constructs (variables, types, expressions, assignments, functions, control structures, etc.), fundamental data structures (arrays, strings, composite data types), basic sorting, and recursion.

In this course, students complete a long-term individual project in order to demonstrate independence and originality, to plan and organize a large project over a long period, and to put into practice knowledge, skills, and research methods. Students are able to submit an original proposal, or browse from projects proposed by prospective supervisors.

This course introduces students to the core ideas and fundamental concepts behind machine learning. Students learn different machine learning problems and the algorithms that exist to address them. They formulate machine learning problems and machine learning pipelines, apply suitable algorithms to tackle different machine learning tasks, implement machine learning algorithms to solve supervised learning problems, and assess appropriate methodologies to evaluate machine learning algorithms.

This course examines various concepts related to software, system and network security. It covers a range of topics including attacks on privacy and attack surface, static and dynamic analysis of malware, hardware security (trusted computing base, secure boot, and attestation), network security and some hot topics in cryptography including elliptic curve, blockchain and bitcoin.

This course explores industrial practices for working on large, existing, software systems. Students discuss how to successfully design, modify, maintain, and operate the large software systems that form so much of the infrastructure of trade, commerce, communication, and entertainment in the modern world. Students also consider current issues faced by the practicing software engineer, and particularly look at engineering trade-offs in different situations and understand that software engineering problems do not always have right and wrong answers.

This course offers a study of basic Machine Learning techniques, when to use Machine Learning on real problems, how to determine which technique is appropriate for each problem, and to apply the techniques in a practical way to real problems. Topics include: learning decision trees and rules; methodological aspects; learning regression trees and rules; ensembles of learning methods; frequent itemsets and association rules; reinforcement learning; relational learning.

The course aims to find solutions to problems using computer languages. Students learn how to solve a problem, and how to design and implement programming, including the implementation. The lectures use the 'C' language. 

'C++', 'Java', 'Python' and 'JavaScript' are introduced in the course. 

The course teaches students a thorough understanding of high-performance and energy-efficient computer architecture. Students learn principles and techniques for evaluating architectural proposals, explore how knowledge of computer architecture informs software performance engineering, and gain a deep understanding of topical trends in advanced computer architecture, compiler design, operating systems, and parallel processing

This lab course (Praktikum) trains in video encoding and transmission over communication networks. A particular focus will be on wireless and mobile networks, which are becoming increasingly important. After a successful completion the students are capable of encoding video clips, assessing the video quality using objective video quality metrics, and streaming the video. The students will further acquire the basics in the field of wireless communication - interference, broadcast communication medium, rate and power control. They will build up technical expertise on MAC and routing protocol behaviour in wireless mesh networking environments through various experiment set-up and performance evaluations.