Computer Science

This course brings together as a coherent body of knowledge the game theoretic algorithms and models that underpin several flourishing subjects at the intersection of computer science, economics, e-commerce, and AI.

This course provides research training for exchange students. Students work on a research project under the guidance of assigned faculty members. Through a full-time commitment, students improve their research skills by participating in the different phases of research, including development of research plans, proposals, data analysis, and presentation of research results. A pass/no pass grade is assigned based a progress report, self-evaluation, midterm report, presentation, and final report.

Complex Systems consist of many interacting constituents and their collective behavior, such as the brain, cities, climate, ecosystems, economy, and traffic. While these systems seem vastly different on first sight they share many features. To familiarize students with all properties of complex systems , this course consists of three pillars: network theory, evolution in spatially extended ecosystems, and collaboration. The course uses computer models to study conflict of interest. This course uses computer programs coded in Python, although working knowledge in Python is not a prerequisite. Each of the three parts concludes with an exam and hand-in exercises. The course concludes with a report written over a small project carried out in a group.

This course focuses on approaches relating to representation, reasoning, and planning for solving real world inference. The course illustrates the importance of using a smart representation of knowledge such that it is conducive to efficient reasoning, and the need for exploiting task constraints for intelligent search and planning. The notion of representing action, space, and time is formalized in the context of agents capable of sensing the environment and taking actions that affect the current state. There is also a strong emphasis on the ability to deal with uncertain data in real world scenarios, and the planning and reasoning methods needed for inference in probabilistic domains. 

This course covers some of the linguistic and algorithmic foundations of natural language processing (NLP). It builds on algorithmic and data science concepts developed in previous courses, applying these to NLP problems. It also equips students for more advanced NLP courses. The course is strongly empirical, using corpus data to illustrate both core linguistic concepts and algorithms, including language modeling, part of speech tagging, syntactic processing, the syntax-semantics interface, and aspects of semantic and pragmatic processing. The theoretical study of linguistic concepts and the application of algorithms to corpora in the empirical analysis of those concepts are interleaved throughout the course.

This course covers automata over infinite words: acceptance conditions, expressiveness, algorithms, and constructions. Topics include translation between types of automata; temporal logic: linear temporal logic (LTL), monadic second-order logic (MSO), and the fragment S1S; translation between logics and automata; LTL model checking; games: infinite games on graphs; solving reachability, Buchi, and parity games; and LTL synthesis using parity games.