Resources for PhD students
Books about probability, bandits and reinforcement learning.
- Foundations of Modern Probability (3rd ed.), Olav Kallenberg (2021). The answer to every question about probability and measure theory.
- Bandit Algorithms. Tor Lattimore and Csaba Szepesvari (2019). A comprehensive overview of regret minimization in bandits.
- Reinforcement Learning. Richard Sutton and Andrew Barto (2018 edition).
- Reinforcement Learning Algorithms. Csaba Szepesvari (2009).
- Markov Decision Processes. Martin Puterman (1994). Everything you need to know about MDPs.
Resources about Git:
- Git Cheat Sheet. A quick reference with the common commands you'll need.
- Github Docs. All you need to know about git and github, and probably much more.
Non-exhaustive list of machine learning conferences:
- NeurIPS. Top tier ML conference. All topics. 10000+ attendees.
- ICML. Top tier ML conference. All topics. 10000+ attendees.
- COLT. Top learning theory conference. Focus on theory. Around 400 attendees.
- AISTATS. Good ML conference. All topics. A few thousands attendees (?)
- ALT. Good learning theory conference. Focus on theory. 100+ attendees.
Some machine learning journals:
- JMLR. Very good ML journal.
- TMLR. Newer ML journal. Not as selective. Quicker reviews.
