Machine Learning Lecture Notes by Yevgeny Seldin cover the material taught by Yevgeny in ML-A, ML-B. If you have not taken ML-A and ML-B before taking ATML you should read the following chapters from the lecture notes:
1 (Supervised Learning)
2.1 + 2.2 + 2.3 (Markov's, Chebyshev's, and Hoeffding's inequalities)
3.1 + 3.2 + 3.3 + 3.4 (Occam's razor bound)
3.5 + 3.6 + 3.7 (VC analysis). This is not strictly necessary but will surely help you enjoy the course to a larger degree.
Appendix B + Appendix C (Probability Theory basics and Linear Algebra basics)
You should also solve the self-preparation assignment for ATML. If you are unable to solve the self-preparation assignment we strongly advise you taking ML before joining ATML.
Programming Language: The working language of the course is Python. All our examples and help are provided in Python and it is recommended to be familiar with Python before starting the course. The self-preparation assignment includes a few programming tasks; if you can code them in Python, you should be fine. If you prefer solving the course assignments using another mainstream language, for example, R, MATLAB, C/C++, you can do so at your own risk and responsibility. Be aware that some things that we may use, for example, Pytorch, may not have support in other programming languages.