Computer Science

This course covers the basics of mathematical and logical foundations of theoretical computer science and the distinction between syntax and semantics. Students acquire the ability of structured reasoning in the sense of carrying out simple mathematical proofs, and they are able to apply simple abstraction techniques to switch between propositions at different levels of abstraction. They master the treatment of formal languages with their counterparts of grammars, finite automata, and push-down automata. Course topics include sets, logical propositions, proof notation, and proof techniques; relations, orders, maps, equivalences, quotients, and cardinality; words, languages, and expressions; Chomsky-hierarchy, grammars, and syntax trees; automata, push-down automata, and pumping lemma; and non-determinism.

This course introduces the basic database concepts such as relational databases, normal forms, and transactions. In addition, the course covers system development (basic software development) and version control, and includes the practical development of a smaller system (web system, mobile system, etc.) as project work.

This course studies the principles of computer systems supporting the operation of application software. Students master the basic overview of a computer system and use these concepts to solve practical problems. The course covers representation of information in computer, machine representation and organization of executable programs, principle of processor, characteristics of hierarchical storage and cache principle, virtual memory, performance optimization.

The purpose of the course is to introduce non-Computer Science students to probabilistic data modelling and the most common techniques from statistical machine learning and data mining. It provides a working knowledge of basic data modelling and data analysis using fundamental machine learning techniques. Topics include: foundations of statistical learning, probability theory; classification methods, such as Linear models, K-Nearest Neighbor; regression methods, such as Linear regression; Bayesian Statistics; clustering; dimensionality reduction and visualization techniques such as principal component analysis (PCA).