We will mainly focus on home-grown notes that have been developed by
faculty for this course at the University of Toronto. Therefore
there is no required textbook for the course.
Nevertheless, several books do exist that serve as excellent
reference texts. I would recommend the following (in order) for
- K. Murphy. Machine Learning: A Probabilistic Approach, 2012.
- C. Bishop. Pattern Recognition and Machine Learning. Springer, 2008.
- T. Mitchell. Machine Learning. McGraw-Hill, 1997.
- D. MacKay. Information Theory, Inference and learning Algorithms.
Cambridge, 2003. (
free online copy)
Below are some other electronic references that you may find useful: