CS489/698 Winter 2018 - Introduction to Machine Learning

There are many good references for machine learning. We will cover material in different textbooks. The first three textbooks are freely available online. Complementary readings in the textbooks are assigned for every lecture in the course schedule.

• [GBC] Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning (2016) freely available online
• [HTF] Trevor Hastie, Robert Tibshirani and Jerome Friedman, Elements of Statistical Learning (2nd edition, 2009) freely available online
• [D] Hal Daume III, A Course in Machine Learning (in progress) freely available online
• [B] Christopher Bishop, Pattern Recognition and Machine Learning (2006)
• [RN] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd Edition) (2010)
• [M] Kevin Murphy, Machine Learning: A Probabilistic Perspective (2012)
• [MRT] Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, Foundations of Machine Learning (2012)
• [SSBD] Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms (2014)