Topics

Textbook

Our primary source of readings will be Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. MIT Press, 2012. We will also read papers and learn materials that are not yet in textbooks.

Other recommended (but not required) books:

• Machine Learning: The Art and Science of Algorithms that Make Sense of Data by Peter Flach (ISBN 1107422221)
• Pattern Recognition and Machine Learning by Chris Bishop (ISBN 0387310738)
• Machine Learning by Tom Mitchell (ISBN 0070428077)
• Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman (ISBN 0387952845)
• Information Theory, Inference and Learning Algorithms by David MacKay (ISBN 0521642981)
• An Introduction to Computational Learning Theory by Michael Kearns and Umesh Vazirani (ISBN 0262111934)

Topics

• The PAC Learning Framework

• Rademacher Complexity and VC-Dimension

• Boosting and Margins Theory

• Guaranteed Spectral Methods in Unsupervised Learning

• Deep Learning Theory, Generalization of Neural Nets

• Deep Reinforcement Learning (if time allows)