The first Homework is posted: see the file on overleaf. It is due for october 5 (no delay accepted). Very basic examples of python notebook relevant to Homework 1 are given on my github page, see for instance: for a pooling problem or for a sampling one
The second Homework is posted: see the file on overleaf. It is due for october 26 (no delay accepted).
The third Homework is posted: see the file on overleaf. It is due for November 15 (no delay accepted).
The fourth Homework is posted: see overleaf. It is due before the end of the calendar year.
A good book for probability and statistics, accessible to students, is Larry A. Wasserman 's All of Statistics
Monte-Carlo methods are well covered in Werner Krauth's Statistical Mechanics: Algorithms and Computations. His MOOC on coursera is also recommended.
A good introduction to statistical learning is given in Elements of Statistical Learning by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie.
Another great reference is Machine Learning:A Probabilistic Perspective, by Kevin P. Murphy.
Exam from 2018: exam
Exam from 2017: exam