Computer Science

This course provides students with practical knowledge and understanding of basic issues and techniques in data management, with sufficient theory to understand the reasons for these techniques. Topics include conceptual (entity relationship model) and logical design (relational model) of database models, relational database management (data definition, data manipulation, SQL, visual interactive query interfaces), and their use in application development (in particular, data extraction from DBMS to spreadsheets application and data extraction to Web applications). Projects in developing a database within an application form an essential component of this course.

This course explores the fascinating new field of digital mental health, which combines theories and methodologies from psychology and human-computer interaction science. The course starts with a general introduction to how technology has and can be used in the prevention, treatment, and support of common mental health problems (e.g., depression and anxiety). The comprehensive problem-based learning approach allows students to develop a basic understanding of the causes and symptoms of common mental health problems, and explore the active ingredients of traditional (e.g., cognitive behavior therapy) and digital interventions.  Different methods used across the two disciplines to design, develop, and evaluate digital mental health interventions and programs are examined. There are many opportunities to practice and get hands-on experience in applying cross-disciplinary methods. Students take part in co-design workshops, introductory sessions on systematic literature reviews, and develop and pitch their own (non-digital) prototypes and intervention ideas. Towards the end of the course, students are encouraged to think critically about current challenges in the field, including the ethical and moral implications of digital applications, and the future of digital mental health.

This course is about the security of networks of computers and their communications. It describes network fundamentals, and the security of Internet protocols, including wired, wireless, and mobile communication networks. It also describes network attacks and countermeasures, particularly focusing on intrusion detection and prevention systems.

This course offers an introduction to HTML language for building websites from local devices. Each session includes one practice project.

The development of suitable models for describing dynamical systems is a central problem within automatic control, and it is critical for the development of robust and high-performance control laws. When relationships between physical quantities are not fully known, then models and the control laws may instead be generated by measurement data, through system identification, machine learning, or adaptive control. The purpose of the course is to teach the basic principles of how this is done. The first part of the course is devoted to adaptive control and system identification for systems with several input and output signals. The focus is on state-space models and methods for generating these, including grey-box identification. The course describes iterative methods for learning, as well as model reduction for the purpose of reducing the dimension of the state space. The second part of the course is devoted to reinforcement learning. This includes the theory of dynamic programming and various approximate methods thereof. Policy iteration is explained, as well as discrete and continuous path planning. The third part of the course deals with the usage of complete components for the purpose of control, for instance, sensors that have been developed using machine learning.

The course gives an insight into how functional programming often offers a possibility to write shorter and easier-to-understand programs than using the traditional imperative or object-oriented approaches. Course content includes the philosophy of functional languages, the programming language Haskell, language constructs and idioms, higher-order functions, lazy evaluation and infinite data structures, monads and monadic computations polymorphic type systems and type classes, and type analysis and type inference.

This course is an application of cognitive science that discusses the history of human-computer interaction and the future direction of development. Especially with the advent of the Web 3.0 era, the social and emotional development of human beings in the computing environment Interaction, cognitive processes, etc. are changing greatly, and the environment of new mankind such as autonomous driving, smart city, twin world, artificial intelligence and cognitive transformation are redefining the industry, productivity, social relationships and values of the future. This course is based on changes in the computing environment and focuses on the transformation and development of human cognition, especially the development of creativity.

The technological and physical basics of Brain-Computer Interfacing will be elaborated. It covers the path from the (electrical) activity of single neurons and networks via the volume conduction of the human head. At the end of the class, students will know the essential physical background of Brain-Computer Interfacing (BCI). They will understand the pathway from the activity of single neurons to the signal of the electroencephalogram (EEG) They will be capable of programming simulations of the electrical properties of the human head as well as simple neural and neural network models.

The course is intended to introduce some basic formal concepts and terminology pervading all areas of computer science, and to establish a common lexicon, including notational conventions and nomenclature, that subsequent courses can build upon. This includes an introduction to abstract set theory, relations, functions, ordered sets, Boolean algebra, logic, and proof techniques, as well as structures such as graphs and trees. Furthermore, the course discusses basic algorithms on graphs, an introduction to combinatorics, some fundamental proof strategies, and basic order structures such as lattices and complete partial orders (CPOs).

 

The lecture covers elementary concepts in machine learning and their application on real data with a special focus on methods that are simple to implement. The course alternates lectures and practice sessions. In the practice sessions, students implement and apply machine learning algorithms on real data in Python. Topics include: supervised learning (linear regression techniques, linear classification, kernel based regression), unsupervised learning (principal component analysis, clustering), and model selection.