1. CST4800 Frontiers in Computer Science and Technology
Course coordinator: Tailin WU
Course credits: 2
Course time：Thursdays 15:10-16:55
Course venue：E10-306, Yungu Campus
This course focuses on the current cutting-edge technologies in the field of computer science and technology, such as Artificial Intelligence, Deep Learning, AI for Science, etc. The lectures are divided into twelve topics, including Frontiers of Deep Learning, Generative Models, Large Models, Reinforcement Learning, Computer Vision and Autonomous Driving, AI + Life Sciences, AI + Scientific Computing, AI + Materials, etc. The content of each topic includes: the development history of theories/technologies, core concepts, underlying ideas and principle mechanisms, the latest research work and technology applications, technology development trends and/or future outlooks, and so on.
2. CST5020 Optimization and Applications
Course coordinator: Fangzhou Xiao
Course credits: 3
Course time：Thursdays 09:50-12:15
Course venue：E10-305, Yungu Campus
Introduction to concepts and methods in optimization, especially convex optimization. Focuses on formulating problems in applications into optimization problems. The basic analysis methods for optimization problems needed for this goal will be covered, such as optimality conditions, duality theory, theorems of alternatives, and so on. The last one-third of the course will focus on in-depth applications of optimization methods we learned to specific domains, such as control, bioengineering, machine learning, and finance, featuring guest lecturers that are cutting-edge practitioners of optimization in these domains.
3. CST5022 Quantitative principles in biological systems
Course coordinator: Po-Yi HO
Course credits: 3
Course time：Firdays 14:20-16:55
Course venue：E10-212, Yungu Campus
The course aims to provide a common "ruler" for researchers from diverse backgrounds to reason about biological systems. Topics are organized around broadly applicable themes like how biological systems deal with noise or optimize functions. Model systems span across scales from chemotaxis to proteins and microbiomes. Emphasis is on active learning through building models and analyzing data together.