博士生课程
1. CST5022 生物系统里的定量原理
课程负责人：何柏毅
课程学分: 3
课程时间：星期五 14:20-16:55
课程地点：E10-212，云谷校区
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.
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2. CST4800 计算机科学与技术前沿
课程负责人：吴泰霖
课程学分: 2
课程时间：星期四 15:10-16:55
课程地点：E10-306，云谷校区
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.
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本科生课程
1. SCI1002 AI+科学
课程负责人：吴泰霖
课程学分: 1
课程时间：星期四 18:30-20:05
课程地点：E10-306，云谷校区
随着人工智能（AI） 的迅猛发展， 人工智能与各门科学的交叉融合逐渐成为一个显著的科学研究趋势。 本课程面向所有专业的本科生或者研究生， 导论式介绍人工智能与各个前沿科学技术的交叉。包含人工智能与数学、物理、生命科学、材料、航空航天、可控核聚变、量子计算、脑机接口等交叉的当前进展和研究前沿，以及了解这些科学领域的概念和思想如何启发新的人工智能架构和方法。 课程考察为分组选一个领域的文献研读和调研汇报。 本课程不需要人工智能或者相应领域的基础知识，旨在让学生初步认识相应领域，并激发起投身于这些领域研究的浓厚兴趣。
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2. PHY3005 热力学与统计力学
课程负责人：汤雷翰
课程学分: 4
课程时间：星期一 16:10-17:45 | 星期二 09:50-11:25
课程地点：E10-305，云谷校区
In this course, students will gain a comprehensive understanding of the principles and tools of thermodynamics and statistical mechanics. This knowledge will enable them to analyze macroscopic properties of physical systems based on microscopic interactions. They will develop critical thinking and problem-solving skills by applying theoretical concepts and formalism to selected classical and quantum systems. While most discussions will focus on systems in thermal equilibrium, we will also touch upon dissipative processes towards equilibrium, such as particle collisions and Boltzmann’s H-theorem. Examples drawn from various fields of physics will illustrate the prowess of the statistical approach in capturing emergent behavior from simple units—a concept increasingly relevant in engineering, social sciences, and biological sciences.
Students will engage in various learning methods, including lectures, tutorials, and miniprojects. Lectures are designed to elucidate core concepts, physical reasoning, and mathematical derivations, while tutorials use exercises and case studies to enhance skills in applying theory to problem-solving contexts. Assessments will consist of problem sets, projects, and midterm and final exams to evaluate students' proficiency in applying theoretical concepts and mathematical manipulations. This dynamic learning environment will prepare students to delve deeply into theoretical approaches, navigate interconnected concepts, and innovate in applying physical principles and quantitative analysis to complex problems in their future careers.
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