We will update this page with additional resources. We encourage you to share other relevant resources that you have found useful and we will add to this list.

Text books

There will be no required text books for the class. Some of the class material, however, will be based on content from the following books (none of which you are required to purchase):

  1. Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001.

  2. Tom Mitchell, Machine Learning. McGraw-Hill, 1997. 1st edition.

  3. Christopher Bishop, Pattern Recognition and Machine Learning. Springer 2007.

  4. Hal Daumé, A Course in Machine Learning

  5. Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms

Linear algebra resources

Probability resources

Other topics

Propositional logic: It may be useful to know about propositional logic to follow certain lectures. Here are some refreshers about propositional logic:

  • Chapter 7 of Stuart Russel and Peter Norvig’s Artifical Intelligence: A Modern Approach gives a good background on propositional logic

  • A lecture on propositional logic in CMU’s program verification class

  • An archive of CS 103 at Stanford, which talks about the mathematical foundations of computing. Look at lectures 8,9 and 10.

  • The propositional logic lecture in the class Logic in Computer Science at the University of Liverpool.

Information Theory:

Miscellaneous resources