Machine Learning Refined: Foundations, Algorithms, and Applications

Book Description

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, 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 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, , 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

Table of Contents

Chapter 1 Introduction
Part I Fundamental tools and concepts
Chapter 2 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 operations
Appendix B Basics of vector
Appendix C Fundamental matrix factorizations andthe pseudo-inverse
Appendix D Convex geometry

Book Details

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