As rm999 points out, this book is vastly more useful than limiting distinctions such as supervised/unsupervised learning (what happens in brains is learning whi

As rm999 points out, this book is vastly more useful than limiting distinctions such as supervised/unsupervised learning (what happens in brains is learning while predicting, which is not completely well captured by that delineation, nor even fully by reinforcement learning). This book will provide a set of skills which will age far better than if it had been specific to some machine learning framework or ideas. It's one of the best I've seen on reasoning probabilistically, bayesian networks, graphical models and probabilistic programming generally. It also teaches the core of the involved algorithms. These skills will be important going forward as we seek to implement ever more brain like systems (and better). The knowledge will also carry over to gaussian processes (which are a subset really) and the more future proof generative deep learning ideas. It also teaches how to reason about your problem and diagnose machine learning systems. Whether you're designing features, trying to figure out how to make a research paper work in real life, or are one of the rare people capable of coming up with deep learning architectures, what the book teaches will be indispensable to you.