Our primary source of readings will be Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. MIT Press, 2012. We will also read papers and learn materials that are not yet in textbooks.
Other recommended (but not required) books:
For RL, here is an ICML 2023 tutorial by John Langford and Alex Lamb
Here are some good RL books that you can consult:
Papers to be discussed will be made available to ahead of time.
Useful inequalities cheat sheet (by László Kozma)
Concentration of measure (by John Lafferty, Han Liu, and Larry Wasserman)