Machine learning refined: foundations, algorithms, and applications

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study. -- Provided by publisher.

"The second edition of this text is a complete revision of our first endeavor, with virtually every chapter of the original rewritten from the ground up and eight new chapters of material added, doubling the size of the first edition. Topics from the first edition, from expositions on gradient descent to those on One-versus- All classification and Principal Component Analysis have been reworked and polished. A swath of new topics have been added throughout the text, from derivative-free optimization to weighted supervised learning, feature selection, nonlinear feature engineering, boosting-based cross-validation, and more"--

First edition published 2016.

Introduction to machine learning -- Part I. Mathematical Optimization: Zero order optimization techniques -- First order methods -- Second order optimization techniques -- Part II. Linear Learning: Linear regression -- Linear two-class classification -- Linear multi-class classification -- Linear unsupervised learning -- Feature engineering and selection -- Part III. Nonlinear Learning: Principles of nonlinear feature engineering -- Principles of feature learning -- Kernel methods -- Fully-connected neural networks -- Tree-based learners -- Part IV. Appendices: Appendix A. Advanced first and second order optimization methods -- Appendix B. Derivatives and automatic differentiation -- Appendix C. Linear algebra.