Useful Resources

Some good textbooks I’ve used…

Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction, 2011.
Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006.
Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. Mining of massive datasets. Cambridge university press, 2014.

Boyd, Stephen, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.
Bertsimas, Dimitris, and John N. Tsitsiklis. Introduction to linear optimization. Vol. 6. Belmont, MA: Athena Scientific, 1997

Great introduction to ML that covers a lot of topics: Video: youtube link Notes: http://cs229.stanford.edu/syllabus.html
Really nice introduction to neural networks that I like to recommend to students: CS231n: Convolutional Neural Networks for Visual Recognition

When I started coding many years ago, I used this short-course to get started. MIT Open Courseware (6.189): A Gentle Introduction to Programming Using Python