Computer Science

This course builds up a toolbox of numerical optimization methods for building solutions in future studies, thereby making it an ideal supplement for students from many different fields in science. The course is taught both at a theoretical level that goes into deriving the math, and also on an implementation level with focus on computer science and good programming practice. Students participate in weekly programming exercises where they implement the algorithms and methods introduced from theory, and apply their own implementations to case-study problems like computing the motion of a robot hand or fitting a model to highly non-linear data. Topics include: first order optimality conditions, Karush-Kuhn-Tucker conditions, Taylors theorem, mean value theorem, nonlinear equation solving, linear search methods, trust region methods, linear least-squares fitting, regression problems, and normal equations.

This course examines database systems. It covers data models: entity-relationship, relational, object-oriented; relational database management systems: data definition, query languages, development tools; database application design and implementation; architecture of relational database management systems: storage management, query processing, transaction processing; lab: design and implementation of a database application.

Intelligent vehicles can communicate with other vehicles or roadside units and behave autonomously. They are believed to significantly change the way that people move from one place to another. This class introduces fundamental knowledge in intelligent vehicles and then focuses on some specific advanced topics (e.g., security). The knowledge and topics bring state-of-the-art technology to students and develop their skills in system modeling, design, and analysis.

There are mainly four parts in this class:
(1) Background: This part introduces traditional (i.e., without connectivity and autonomy) system architecture, vehicular networks, and basic design and analysis approaches.
(2) Applications: This part introduces applications of intelligent vehicles, including advanced driver-assistance systems, cooperative adaptive cruise control, and intersection management.
(3) Technology: This part introduces the technology which is needed to realize the applications of intelligent vehicles.
(4) Advanced Topics: This part introduces advanced topics such as over-the-air update, security, and certification.

Depending on students' interests, final projects can be survey, implementation, or research. 

This course examines the fundamentals of human-computer interaction to design, develop, and evaluate interactive systems based on information technologies that ensure the accessibility, ergonomics and usability of the systems. Topics include: introduction to human-computer interaction (HCI); interaction models and metaphors; user-centered design; assessment of interactive systems. 

This course offers an introduction to the fundamental principles and concepts of computers and programming with the programming language Python.

Responsible Data Science is examined through the lens of four introductory dimensions: data dimension; algorithm dimension; human dimension, including psychology of human biases and ethics or moral philosophy; design dimension, including data visualization and interaction design and explainable artificial intelligence (XAI).

Throughout this course, students follow lectures and workshops, read literature, engage in class discussions, give presentations, critique, and conduct an investigation on a topic related to a (self-chosen) real-world ethical problem related to data science in a particular domain. The project also contains a practical solution to the problem illustrated in a low-fidelity prototype.