Machine Learning: A Probabilistic Perspective

Attempting to self study statistics and econometrics in a serious way. I just finished Casella and Berger's "Statistical Inference". I'm about halfway through Shumway and Stoffer's "Time Series Analysis", but then next on the list, in the order I tend to read them: - Theory of Point Estimation Casella/Lehmann - Bayesian Data Analysis 3rd ed. - Convex Optimization - Boyd - Econometrics - Hayashi - New Introduction to Multiple Time Series Analysis - Elements of Statistical Learning - Machine Learning: A probabilistic perspective There's a fair amount of overlap between these books, so it's not quite as much as it seems. But i'm hoping to make it through at least a chapter a week this year, which should get me most of the way through them. We'll see how it goes.

1. The Goodfellow book is the obvious one. Another option is 'Machine Learning: A probabilistic perspective' by Kevin Murphy, which does have exercises but has less material on CNNs IIRC and is slightly older. Also Bishop 'Pattern Recognition and Machine Learning' is a classic reference but even older. 2. The Pedro Domingos book the Master Algorithm tries to address this itch / may be less philosophical that you want. It is >okay< but I don't think people love it.

I liked all three of them. I also feel Murphy's MLAPP provides additional useful material and is well written.

Machine Learning: a Probabilistic Perspective http://www.cs.ubc.ca/~murphyk/MLbook/ excellent ML textbook, comes with code