Seminars

This seminar discusses the cutting edge of artificial intelligence, including large language models, reinforcement learning, and generative models.

This seminar discusses “classical statistical methods in Euclidean space” and “modern statistical methods in non-Euclidean space”, aiming to expand technical tools from classical methods and focus on the novel non-parametric statistical methods in recent years.

This seminar explores key optimization algorithms, from convex programming to stochastic methods and neural network generalization.

This seminar provides a systematic introduction to statistical computing with Python, covering deep learning, reinforcement learning, and distributed computing techniques.

This seminar introduces the integration of mechanistic models with statistical inference, covering key methodologies and applications.

We systematically introduces deep learning fundamentals.

We cover selected topics in Kernel Learning and research progress in Nonparametric Statistics.

We present the basic tools in Optimization and listen the lectures presented by Prof. Bingsheng He.

We cover selected topics Reinforcement Learning.