机器学习理论

General

• 张潼 - Mathematical Analysis of Machine Learning Algorithms

• 文再文 - 大数据分析中的算法

• 滕佳烨 - 机器学习理论简明手册

• NYU Mathematical Tools for Data Science

• Stanford CS229M Machine Learning Theory

• Stanford STATS385 Analyses of Deep Learning

• MIT 6.438 Algorithms for Inference

• Caltech CS159 Neural Network Theory: Learning & Generalisation

• NYU DA-GA3001 Modern Topics in Statistical Learning Theory

• Cornell CS4787 Principles of Large-Scale Machine Learning

Blogs & Seminars

• International Seminar on Foundational Artificial Intelligence

• NYU Math and Data Seminar

• Princeton Machine Learning Theory Summer School

• 苏剑林 - 科学空间

• Lilian Weng's Blog

• Building Blocks for AI Systems

• RLChina 强化学习社区

Reinforcement Learning

• Princeton ECE524 Foundations of Reinforcement Learning
• UIUC CS598 Statistical Reinforcement Learning
• Stanford CS234 Reinforcement Learning
• Cornell CS6789 Foundations of Reinforcement Learning
• Caltech CS159 Predictive Control & Model-based Reinforcement Learning
• Dimitri Bertsekas - Reinforcement Learning

Generative Models

• Stanford CS236 Deep Generative Models
• Cornell CS6785 Deep Generative Models
• CMU 10-708 Probabilistic Graphical Models
• UW CS599 Generative Models

Probability & Statistics

• CMU 36-708 Statistical Methods for Machine Learning
• UW-Madison STAT710 Mathematical Statistics
• Stanford Statistical Learning with Sparsity
• Gaussian Process Summer Schools
• PKU Modern Computational Statistics
• UCL GR8201 Bayesian Nonparametrics
• UCB Stat240 Robust and Nonparametric Statistics
• Yale ECE598 Information-theoretic Methods in High-Dimensional Statistics
• MIT 18.S997 High-Dimensional Statistics
• UCI High-Dimensional Probability and Applications in Data Science

Algorithms

• Caltech CS159 Data-Driven Algorithm Design
• Harvard CS229r Algorithms for Big Data
• UCB CS294 Sketching Algorithms
• Simons Institute Sketching and Algorithm Design
• UW CS422 Toolkit for Modern Algorithms

Causal Inference

• Mila Introduction to Causal Inference
• UCI CS295 Causal Reasoning
• UPenn A Crash Course in Causality: Inferring Causal Effects from Observational Data

Others

• Simons Institute Online Learning & Convex Optimization
• Simons Institute Geometric Methods in Optimization and Sampling
• Caltech CS159 Online Learning, Interactive Machine Learning, and Learning from Human Feedback
• EPFL MATH512 Optimization on Manifolds
• Stanford EE274 Data Compression, Theory and Applications
• MIT Introduction to Data-Centric AI
• UW Sparsity and Compression Sensing
• UW CS599 Sparsification, Sampling, and Optimization
• UW-Madsion CS728 Integer Optimization