Computer Science

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.

This course provides an introduction to modern cryptography with a mathematical focus. It covers the basics of abstract algebra and number theory, and introduces cryptocurrencies such as Bitcoin, BlockChain, and FinTech. Topics include data security, stream ciphers, Data Encryption Standard (DES) and alternatives, Advanced Encryption Standard (AES), block ciphers, public-key cryptography, RSA Cryptosystem, elliptic curve cryptosystems, digital signatures, hash functions, Message Authentication Codes (MACs), and key establishment.

This course covers the basics of programming with Python. The course uses Python to create some basic applications for Data Science use cases. The focus of this course is to learn how to program with Python. Hence, the course focuses the basics of the python programming language as well as ways to structure code or application repositories, debug implementations, and test the functionality of code and programs.

The undergraduate research program places students in research opportunites to conduct indpendent research under the supervision of a Chinese University of Hong Kong faculty. Students are expected to spend approximately 15 to 20 hours per week in independent research as well as attend lectures and labs.