Machine Learning Refined
Foundations, Algorithms, and Applications

An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.

Jeremy Watt (Author), Reza Borhani (Author), Aggelos K. Katsaggelos (Author)

9781108480727, Cambridge University Press

Hardback, published 9 January 2020

594 pages, 316 colour illus. 127 exercises
25.5 x 18.3 x 2.9 cm, 1.36 kg

'This is a comprehensive textbook on the fundamental concepts of machine learning. In the second edition, the authors provide a very accessible introduction to the main ideas behind machine learning models.' Helena Mihaljevi?, zbMATH

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.

1. Introduction to machine learning
Part I. Mathematical Optimization: 2. Zero order optimization techniques
3. First order methods
4. Second order optimization techniques
Part II. Linear Learning: 5. Linear regression
6. Linear two-class classification
7. Linear multi-class classification
8. Linear unsupervised learning
9. Feature engineering and selection
Part III. Nonlinear Learning: 10. Principles of nonlinear feature engineering
11. Principles of feature learning
12. Kernel methods
13. Fully-connected neural networks
14. 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.

Subject Areas: Signal processing [UYS], Pattern recognition [UYQP], Machine learning [UYQM], Communications engineering / telecommunications [TJK], Information theory [GPF]