Computer Science

This course addresses a number of topics in computer and network security. Topics include memory errors, Web, network, countermeasures, and pointers to research papers. The course prepares students to identify software vulnerabilities, shows how to address these, and introduces how vulnerabilities are exploited through malware.

This special lab course nurtures international students' creative competency by offering them opportunities for learning in communities of research practice. The student's supervisor arranges the research topic. Students give three oral presentations during the study period. In the presentations, students integrate ideas and analyses on laboratory results into creative and academically coherent work. FrontierLab program coordinators and supervisors attend and evaluate the final oral presentation.

Data science and machine learning are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Good data science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods. This course extends the foundation of probability and statistics with an introduction to the most important concepts in applied machine learning, with social science examples. It covers the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods covered. 

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.