Machine Learning Refined: Foundations, Algorithms, and Applications

Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.  Additional resources including supplemental discussion topics, code demonstrations, and exercises can be found on the official textbook website at mlrefined.com

Table of Contents

Chapter 1 Introduction
Part I Fundamental tools and concepts
Chapter 2 Fundamentals of numerical optimization
Chapter 3 Regression
Chapter 4 Classification
Part II Tools for fully data-driven machine learning
Chapter 5 Automatic feature design for regression
Chapter 6 Automatic feature design for classification
Chapter 7 Kernels, backpropagation, and regularized cross-validation
Part III Methods for large scale machine learning
Chapter 8 Advanced gradient schemes
Chapter 9 Dimension reduction techniques
Part IV Appendices
Appendix A Basic vector and matrix operations
Appendix B Basics of vector calculus
Appendix C Fundamental matrix factorizations andthe pseudo-inverse
Appendix D Convex geometry