Machine Learning: A probabilistic perspective by Kevin Murphy. The material in this book is closest to what we will cover in the course, but is unfortunately not available for free. Written by an academic and a practitioner of machine learning, this text is full of real world examples and applications, while eschewing the ad-hoc approach of other texts.
Deep learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. A new text on this burgeoning field. Written by central figures in the area, this text surveys deep learning, includes many useful citations (a great deal of the literature is still in academic articles), provides good rules of thumb, and is a relatively light read. This book is very much from a practitioner’s perspective with less justification for methodology and less emphasis on mathematical rigor. My limited experience is that other papers in this area have a similar emphasis, which reflects the limited understanding of why these methods work very well in some instances and not so well in others.
Datacamp: An online learning site with an emphasis on data science. Includes mini-lectures and online programming exercises. A nice introduction on how to take the material we are learning and apply it. However, there is a noticeable bump in difficulty in proceeding to implement these methods from scratch on your own computer.
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