Computer Science

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.

This undergraduate research course provides opportunities to conduct independent research under the supervision of a faculty member at the Chinese University of Hong Kong. Participants are expected to devote approximately 20 hours per week to their research project, with the exact time commitment determined in consultation with the faculty supervisor. The course requires the completion of an original research paper. It develops research methods, analytical thinking, academic writing, and the ability to communicate research findings effectively through scholarly papers and reports. As an independent research program, the course emphasizes initiative, self-directed learning, and regular consultation with the faculty supervisor throughout the research process.