Textbooks

[textbooks I have been reading]

• Convex Optimization

  • Lectures on Convex Optimization, by Yurii Nesterov
  • Convex Optimization: Algorithms and Complexity, by Sébastien Bubeck

• Reinforcement Learning

  • Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto
  • Reinforcement Learning: Theory and Algorithms, by Alekh Agarwal, Nan Jiang, Sham M. Kakade, and Wen Sun
• Bandit Problems
  • Introduction to Multi-Armed Bandits, by Aleksandrs Slivkins
  • Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems, by Sébastien Bubeck and Nicolo Cesa-Bianchi
• High-dimensional Statistics
  • High-Dimensional Probability: An Introduction with Applications in Data Science, by Roman Vershynin
• Learning Theory
  • Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David

Course Websites

[online courses I followed]

• ELE522: Large-Scale Optimization for Data Science- Yuxin Chen (Princeton University)
• CS 6789: Foundations of Reinforcement Learning- Wen Sun (Cornell University) and Sham Kakade (University of Washington)

Blogs

• Off the Convex Path