Things I think are worth reading. Consider leaving a Star if this helps you.

# Statistical Learning Theory #

- Shalev-Shwartz, Shai, and Shai Ben-David.
**Understanding machine learning: From theory to algorithms.**Cambridge university press, 2014. [book] [lectures] - Abu-Mostafa, Yaser S., Malik Magdon-Ismail, and Hsuan-Tien Lin.
Vol. 4. New York, NY, USA:: AMLBook, 2012. [book]**Learning from data.** - ISIT 2018 - S. Kannan, H. Kim & S. Oh -
[video] [slides]**Deep learning and information theory An Emerging Interface** - Ashish Khisti. ECE1508:
[course]**Introduction to Statistical Learning**

# Information Theory #

- Shannon, Claude Elwood.
Bell system technical journal 27.3 (1948): 379-423. [paper]**“A mathematical theory of communication.”** - Cover, Thomas M., and Joy A. Thomas.
John Wiley & Sons, 2012.**Elements of information theory.** - Gleick, James.
Vintage, 2012.**The information: A history, a theory, a flood.**

# Probabilistic Graphical Models #

- MAC6916:
[course]**Probabilistic Graphical Models** [code] [docs]**DAFT Beautifully rendered probabilistic graphical models.**

# Deep Learning Theory #

- Goodfellow, Ian, Yoshua Bengio, and Aaron Courville.
MIT press, 2016. [book]**Deep learning.**

# Machine Learning for Health #

- CSC2541HS:
[course]**Topics in Machine Learning** - Ghassemi, Marzyeh, et al.
arXiv preprint arXiv:1806.00388 (2018). [paper]**“Opportunities in machine learning for healthcare.”**