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Explore how and why machine learning algorithms work with this self-contained, hands-on textbook for senior undergraduate and graduate students. Using Matlab and Python, it includes over 85 worked examples demonstrating how to implement algorithms, and over 75 end-of-chapter problems empowering students to develop their own code.
List 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; Part VII. Large Language Models: 21. Large Language Models; Appendix A. A Review of Linear Algebra; Appendix B. A Review of Probability and Statistics.
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).
