This book is focused on the practical side of machine learning. We did not present the thinking behind the algorithms or the theory that justify them. If you are interested in that aspect of machine learning, we recommend Pattern Recognition and Machine Learning, by Christopher Bishop. This is a classical introductory text in the field. It will teach you the nitty-gritty of most of the algorithms we used in this book.

If you want to move beyond the introduction and learn all the gory mathematical details, Machine Learning: A Probabilistic Perspective, by Kevin P. Murphy, is an excellent option (www.cs.ubc.ca/~murphyk/MLbook). It's very recent (published in 2012) and contains the cutting edge of ML research. This 1,100-page book can also serve as a reference, as very little of machine learning has been left out.

Specific to deep learning, you probably want to read Deep Learning, by Ian Goodfellow et al. (http://www.deeplearningbook.org). The book is more on the theoretical side, but still very accessible. Its web version is free of charge, but some books are just worth the investment.