A collection of online courses related to machine learning, finance, economics, statistics.
ML Basics
- CMU Machine Learning 10-601 by Tom Mitchell and Maria-Florina Balcan (Spring 2015, video)
Convex Optimization
- CMU Convex Optimization 10-725 by Ryan Tibshirani (Fall 2018, video)
Statistical Learning Theory
- Stanford Statistical Learning Theory CS229T/STATS231 by Tengyu Ma (Autumn 2018-2019)
- CMU Advanced Introduction to Machine Learning 10-715 by Maria-Florina Balcan (Fall 2018)
High Dimension
- Princeton Mathematics of High-Dimensional Data ELE538 by Yuxin Chen (Fall 2018)
Bandit
- Berkeley Learning in Sequential Decision Problems CS294/Stat260 by Peter Bartlett (Fall 2014)
- UMD Advanced Topics in Theory of Computing: Bandits, Experts, and Games CMSC 858G by Alex Slivkins (Fall 2016)
- Columbia Bandits and Reinforcement Learning COMS E6998.001 by Alekh Agarwal and Alex Slivkins (Fall 2017)
DP and Stochastic Control
- MIT Dynamic Programming and Stochastic Control 6.231 by Dimitri Bertsekas (Fall 2015, video)
- Stanford Stochastic Control EE365 by Sanjay Lall (Spring 2014)
RL
- UIUC Statistical Reinforcement Learning CS598 by Nan Jiang (Fall 2020)
- Stanford Reinforcement Learning CS234 (Winter 2020, video)
- Arizona State University Reinforcement Learning And Optimal Control by Dimitri Bertsekas (Spring 2019, video)
Deep RL
- Berkeley Deep Reinforcement Learning CS294 (Fall 2017, video)