Textbooks: CSCC11 Introduction to Machine Learning

We will mainly focus on notes written by Hertzmann and Fleet at the University of Toronto. These notes will be posted on the main course website. As a consequence, no textbook is a requirement for the course. Nevertheless, many of the following books will serve as excellent reference texts for the subject covered in this course. Many of the topics we cover closely follow material in books by Bishop, MacKay, and Hastie et al, all of which are excellent books but somewhat advanced for this course.

C. Bishop. Pattern Recognition and Machine Learning. Springer, 2008.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer, 2008 (pdf)

T. Mitchell. Machine Learning. McGraw-Hill, 1997.

D. MacKay. Information Theory, Inference and learning Algorithms. Cambridge, 2003. (pdf)

R. Duda, P. Hart, and D. Stork. Pattern Classifcation. Wiley, 2001.

K. Murphy. Machine Learning, MIT Press, 2012. (the second edition is more comprehensive and now released)