09-23-2026, 07:44 PM
A First Course in Machine Learning by Edmond Chow is a mathematically oriented introduction to the fundamental ideas of machine learning, designed primarily for undergraduate students but also suitable for graduate students and practitioners who want a structured introduction.
The book presents machine-learning models and algorithms systematically, using simple examples to build intuition around concepts that can otherwise be difficult to understand. It covers major areas including supervised learning, regression, classification, unsupervised learning, statistical learning, pattern recognition, predictive modeling, and data-driven modeling.
A major emphasis is on understanding the mathematics behind machine learning, rather than treating algorithms as black boxes. The book develops the necessary mathematical concepts beyond linear algebra and multivariable calculus as they are needed, while also including programming exercises that allow readers to experiment with the algorithms without requiring a particular programming language. With 43 progressively arranged chapters and 386 pages, it is intended to provide a foundation for more advanced study in machine learning, data science, mathematics, engineering, and computational science.
BOOK
The book presents machine-learning models and algorithms systematically, using simple examples to build intuition around concepts that can otherwise be difficult to understand. It covers major areas including supervised learning, regression, classification, unsupervised learning, statistical learning, pattern recognition, predictive modeling, and data-driven modeling.
A major emphasis is on understanding the mathematics behind machine learning, rather than treating algorithms as black boxes. The book develops the necessary mathematical concepts beyond linear algebra and multivariable calculus as they are needed, while also including programming exercises that allow readers to experiment with the algorithms without requiring a particular programming language. With 43 progressively arranged chapters and 386 pages, it is intended to provide a foundation for more advanced study in machine learning, data science, mathematics, engineering, and computational science.
BOOK
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