Computer Science

This course examines the data science process. It covers orientation to the use and configuration of core data science toolkits, data collection and annotation fundamentals, principles of responsible data science, the use of quantitative tools in data science, and presentation of data science findings.

This course offers an introduction to robotics. Topics include: perception in robotics; actuation in robotics; navigation; processing elements; decision-making in robotics; human-robot interaction; novel applications.

This course provides an overview of robot mechanisms, dynamics, and intelligent controls. Topics include planar and spatial kinematics, and motion planning; mechanism design for manipulators and mobile robots; multi-body dynamics; control design, actuators, and sensors; sensing and perception to enable intelligent behavior; and computer vision. Weekly laboratories provide experience with servo drives, real-time control, task modelling and embedded software. Students will build working robotic systems in a group-based term project.

This course teaches various algorithms and data structures. As the basis of computer science, it is one of the problems in the Fundamental Information Technology Engineer Examination and is a topic that frequently appears in recruitment (coding interviews) for software engineers.  

Students will be able to master computational concepts such as computational complexity and be able to implement algorithms. In addition, students will be able to design algorithms.

This examines the technical aspects of artificial intelligence from an ethical point of view and the many social and economic issues related to it. 

This course offers a study of interactive ecosystems. Topics include: human-centered informatics; paradigms, styles, and principles of interaction; design approaches; designing and prototyping of interactive ecosystems; evaluation.

This course covers the basic skills needed to create, examine, appreciate, and understand computational art. It covers basic programming skills in Python, experience using tools for live coding music, creating animations, and hands-on experience using generative AI technologies. Topics include strings, programs as files, semantics, functions; conditionals, Iteration, functions vs methods; lists, dictionaries, sets, reading and writing to files; audio programming, sequencing events in time, randomness, signal processing; animations, graphics, user interaction, performance considerations; and introduction to generative AI, stable Diffusion, and text processing. 

The course covers fundamental engineering and mathematical concepts for understanding the wired network technologies, internet architectures and protocols, and networking programming.

This course examines key theories, concepts and industry methods that are crucial to the user-centered design process.