Teaching will always be an indispensable ingredient. My passion for teaching is one of the main reasons for my pursuit of the career as a university professor.
2011-2012, Modern Statistical Methods and Related Theories, Chinese Academy of Sciences, P. R. China.
2016-2017, Statistical Modeling and Inference, Renmin University of China.
Spring 2017 , Bayesian Modeling and Inference, Renmin University of China.
Fall 2017, Large Sample Theory, Renmin University of China.
Fall 2017, Statistical Modeling and Inference, Renmin University of China.
2019-2022, Deep Learning, master Course, Renmin University of China.
In-depth introduction to machine learning in 15 hours of expert videos, taught by Trevor Hastie and Rob Tibshirani. This course is based on the book An Introduction to Statistical Learning. The link is recommended by Xinghao Qiao.
Theory of Statistics, taught by Emmanuel J. Candes.
Convex Optimization, taught by Ryan Tibshirani.
Journal Club: Hot Ideas in Statistics, taught by Ryan Tibshirani.
Advanced Topics on Statistics, taught by T. Tony Cai.
Probability and Statistics, taught by Emmanuel J. Candes .
Deep Learning, A recommended wesite about deep learning.
Advanced Topics: Reinforcement Learning by David Silver @UCL. This link is recommended by Rui Song.
Topic course on Deep Learning by Joan Bruna@UC Berkeley. This link is recommended by Rui Song.
Convolutional Neural Networks for Visual Recognition, at Stanford.
R for Data Science, taught by Harley Wickham.
Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science, taught by Afonso S. Bandeira.
Julia language, ggplot2 and Computational Statistics in Python.
Trevor Hastie's talk on Data Sciences@Stanford and Statistical learning by Larry Wasserman@CMU.