Cambridge University Press Introduction to Machine Learning: From Math to Code

"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"-- 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. 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. Autorid: Ruye Wang