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. Learning from data. Vol. 4. New York, NY, USA:: AMLBook, 2012. [book]
- ISIT 2018 - S. Kannan, H. Kim & S. Oh - Deep learning and information theory An Emerging Interface [video] [slides]
- Ashish Khisti. ECE1508: Introduction to Statistical Learning [course]
- Shannon, Claude Elwood. “A mathematical theory of communication.” Bell system technical journal 27.3 (1948): 379-423. [paper]
- Cover, Thomas M., and Joy A. Thomas. Elements of information theory. John Wiley & Sons, 2012.
- Gleick, James. The information: A history, a theory, a flood. Vintage, 2012.
Probabilistic Graphical Models
- MAC6916: Probabilistic Graphical Models [course]
- DAFT Beautifully rendered probabilistic graphical models. [code] [docs]
Deep Learning Theory
- Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. [book]
Machine Learning for Health
- CSC2541HS: Topics in Machine Learning [course]
- Ghassemi, Marzyeh, et al. “Opportunities in machine learning for healthcare.” arXiv preprint arXiv:1806.00388 (2018). [paper]
Python Data Analysis