Machine.Learning.Refined.Foundations.Algorithms.and.Applications.epub下载

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