Optional Textbooks "Machine Learning: A Probabilistic Approach" by K. Murphy and "Elements of Statistical Learning" by Hastie, Tibshirani and Friedman. For each lecture, I will point out the chapters in these books that are relevant.

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• deeplearning.net hub (some pages slightly outdated)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

If you like videolectures you can check this site

Books in related fields you should have if you are serious about 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.