Introduction to Machine Learning: From Math to Code

by Ruye Wang

Overview

Emphasizing how and why machine learning algorithms work, this introductory textbook bridges the gap between the theoretical foundations of machine learning and its practical algorithmic and code-level implementation. Over 85 thorough worked examples, in both Matlab and Python, demonstrate how algorithms are implemented and applied whilst illustrating the end result. Over 75 end-of-chapter problems empower students to develop their own code to implement these algorithms, equipping them with hands-on experience. Matlab coding examples demonstrate how a mathematical idea is converted from equations to code, and provide a jumping off point for students, supported by in-depth coverage of essential mathematics including multivariable calculus, linear algebra, probability and statistics, numerical methods, and optimization. Accompanied online by instructor lecture slides, downloadable Python code and additional appendices, this is an excellent introduction to machine learning for senior undergraduate and graduate students in Engineering and Computer Science.

Product Details

ISBN-13:	9781316519509
Publisher:	Cambridge University Press
Publication date:	09/30/2025
Pages:	668
Product dimensions:	0.00(w) x 10.00(h) x 0.00(d)

About the Author

Ruye Wang is an Emeritus Professor of Engineering at Harvey Mudd College, with over thirty years of experience in teaching courses in Engineering and Computer Science. Previously a Principal Investigator at the Jet Propulsion Laboratory, NASA, his research interests include image processing, computer vision, machine learning and remote sensing. He is the author of the textbook Introduction to Orthogonal Transforms (2012).

Table of Contents

Part I. Mathematical Foundations: 1. Solving Equations; 2. Unconstrained Optimization; 3. Constrained Optimization; Part II. Regression: 4. Bias-Variance Tradeoff and Overfitting vs Underfitting; 5. Linear Regression; 6. Nonlinear Regression; 7. Logistic and Softmax Regression; 8. Gaussian Process Regression and Classification; Part III. Feature Extraction: 9. Feature Selection; 10. Principal Component Analysis; 11. Variations of PCA; 12. Independent Component Analysis; Part IV. Classification: 13. Statistic Classification; 14. Support Vector machine; 15. Clustering Analysis; 16. Hierarchical Classifiers; 17. Biologically Inspired Networks; 18. Perceptron-Based Networks; 19. Competition-Based Networks; Part VI. Reinforcement Learning: 20. Introduction to Reinforcement Learning.