Schedule

Schedule is subject to changes and adjustments. Links will get updated and filled in as we go. Slides are linked from each lecture.

Readings: Find at least one text per lecture in which you can complete the assigned readings, prior to lecture. A variety of readings are provided; the more you read the better your understanding of the topic. Abbreviations (of reading names) defined at the bottom of the page.

*Readings refer to the recommended texts, as well as the following, and should be completed before lecture:

• AIMA: Russell and Norvig, "Artificial Intelligence: A Modern Approach.'' 3rd Edition, Prentice Hall, 2010.
• D1: Lecture 1 from Dasgupta's course on Machine Learning
• J1: Lecture 1 from Jaakkola's course on Machine Learning
• Mitchell: Machine Learning, McGraw Hill, 1997.
• KV: Kearns and Vazirani: An Introduction to Computational Learning Theory, MIT Press, 1994.
• CLRS: Introduction to Algorithms (Third Edition), by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Cliff Stein. MIT Press, 2009.
• MR: Motwani and Raghavan, "Randomized Algorithms" Cambridge University Press, 1995.
• CIML: Daume: A Course in Machine Learning.
• FML: Mohri, Rostamizadeh, and Talwalkar: Foundations of Machine Learning, MIT Press, 2012.
• HTF: Hastie, Tibshirani, and Friedman: The Elements of Statistical Learning Second Edition, Springer, 2009.
• SB: Sutton and Barto: Reinforcement Learning: An Introduction. 2nd Edition in progress, 2017. Link to draft.