Some Interesting Books/Open courses I have read during my Ph.D. study are stored here (To avoid copyright problems, only names will be provided)
This is somehow just a memo:)
The classifications & names are not very rigorous, Whatever:)
Tong Zhang, Learning Theory (Too many formulas... just)
李航，统计学习方法
Larry Wasserman, All of Statistics
Andrew Gelman, Bayesian Data Analysis
Ma Yi, High-Dimensional Data Analysis
Keener, Theoretical Statistics
Efron, Computer Age Statistical Inference
Sara van de Geer, Empirical Process Theory (God, it's just soooo difficult, I will revisist it someday)
CUHK STAT3009: Recommender System
UCB STAT210B: Theoretical Statistics II (By Peter Bartlett)
PKU: Modern Computational Statistics (2019) (Website: https://zcrabbit.github.io/courses/mcs-f19.html)
Yale SDS610: Statistical Inference (Last chapter very interesting, needs revising for many times)
Bodhisattva Sen: A Gentle Introduction to Empirical Process Theory and Applications (Not gentle at all, very difficult. May revisit the weak convergence part some day).
Bradly Neal, Introduction to Causal Inference
Gabriel Peyre, Computational Optimal Transport
文再文, 最优化：建模，算法与理论
Evans, Introduction to Stochastic Differential Equation
John Conway, Functional Analysis
Rick Durrett, Probability: Theory and Examples
Do Carmo, Differential Geometry of Curves and Surfaces
Yeung Wai-Ho, Information Theory and Network Coding
Roman Vershynin, High-Dimensional Probability
邱锡鹏，神经网络与深度学习
William L. Hamilton Graph Representation Learning
Hands on Machine Learning for Algorithmic Trading
尼尔·盖曼，美国众神
乔治·R·R·马丁, 七王国的骑士
艾萨克·阿西莫夫，神们自己
乔治·R·R·马丁，血与火