Optional Textbook

• All of Nonparametric Statistics by Larry Wasserman

  These machine learning books can supplement the notes on occasional topics

• "Learning Theory from First Principles" by Francis Bach (pdf available)
• "Elements of Statistical Learning" by Hastie, Tibshirani and Friedman. Second Edition. (pdf available)
• "Machine Learning: A Probabilistic Approach" by K. Murphy

Deep learning resources (open source)

• Dive into Deep Learning 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• deeplearning.net hub (some pages slightly outdated)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

Books in related fields you should have if you are serious about Modern Statistical 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 "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
• Finally, don't miss Elements of Information Theory by Cover and Thomas.