Optional Textbooks

• "Mining of Massive Data Sets" by Jure Leskovec, Anand Rajaraman and Jeff Ullman.
  
  Additional material can be found in these textbooks, which are (optional) textbooks for both STAT 535 and CSE 546.
• "Elements of Statistical " by Hastie, Tibshirani and Friedman. Second Edition. (pdf available)
• "Machine : A Probabilistic Approach" by K. Murphy (NOTE -- Second Edition recently appeared)

Deep learning resources (open source)

• Dive into Deep Leaning by Aston Zhang, Zack Lipton, Mu Li, Alex Smola supported by a community of other contributors
• Deep learning book by Ian Goodfellow, Yoshua Bengio and Aaron Courville
• deeplearning.net hub (some pages slightly outdated)

Additional research articles that will be discussed in class will be posted on the Handouts/Course notes page, as will my own course notes.

Software resources

TBPosted

If you like videolectures you can check this site

Books in related fields you should have if you are serious about Machine Learning

• For graph theory a good book is West - "Introduction to graph theory".
• For algorithms and data structures, I recommend the classic CRLS, aka Cormen, Leiserson, Rivest and Stein "Introduction to Algorithms".
• On optimization there is "Convex optimization" by Boyd and Vandenberghe available on the web)
• and the ever useful "Numerical recipes" by Press, Teukolsky, Vetterling and Flannery (NR) which is available on the web (text and code!) (Note: the freely available edition is an older one).
• For more advanced optimization there is Nocedal and Wright
• This course on Learning Theory from First Principles by Francis Bach is on its way to becoming a classic
• Finally, don't miss this classic Elements of Information Theory by Cover and Thomas.