Mathematics for Machine Learning 1st Edition
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Mathematics for Machine Learning 1st Edition
Essential Mathematics for Data Science and Machine Learning
$33.90
$48.90
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Detailed Overview:
Mathematics for Machine Learning (1st Edition) by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong is a highly regarded academic resource designed to help students and professionals build the mathematical foundations necessary for understanding modern machine learning algorithms. Written in a clear and accessible style, the book bridges the gap between abstract mathematical concepts and their practical applications in data science and artificial intelligence.
The book introduces essential topics such as linear algebra, analytic geometry, matrix decompositions, vector calculus, probability, and continuous optimization. Each chapter connects mathematical theory directly to machine learning methods, enabling readers to understand how algorithms work rather than simply applying them mechanically. Real-world examples, intuitive explanations, and practical exercises make the content approachable for beginners while remaining valuable for advanced learners.
Widely used in universities and self-study programs, this book is ideal for computer science students, engineers, data scientists, AI researchers, and professionals transitioning into machine learning. It provides a solid framework for mastering the mathematical principles behind deep learning, predictive modeling, and statistical analysis, making it an essential addition to any technical learning library. 
Product Details:
Title: Mathematics for Machine Learning (1st Edition)
Author: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
ISBN-13: 9781108455145
ISBN-10: 110845514X
Publisher: Cambridge University Press
Binding: Paperback
No of Pages: 417
Language: English
Publisher Date: April 2019
This book provides a comprehensive introduction to the core mathematical concepts required for machine learning and artificial intelligence. It covers key subjects including linear algebra, vector spaces, eigenvalues, calculus, probability distributions, optimization techniques, and geometric interpretations used in machine learning models.
Rather than focusing only on formulas, the authors explain how mathematical concepts are applied in real machine learning algorithms. Readers learn the theoretical foundations behind supervised learning, neural networks, dimensionality reduction, and probabilistic modeling. The book also includes practical examples and exercises that strengthen conceptual understanding and analytical thinking.
Suitable for beginners and intermediate learners, this text helps readers confidently approach advanced AI and data science topics. It is widely recommended for university courses, technical training, and self-paced learning in machine learning and computational mathematics.
Marc Peter Deisenroth is a renowned researcher and educator specializing in machine learning, robotics, and artificial intelligence. He has held academic positions at leading institutions and is recognized for his contributions to probabilistic machine learning and data-driven systems. Along with co-authors A. Aldo Faisal and Cheng Soon Ong, he developed this book to make advanced mathematical concepts accessible to students and professionals entering the field of AI and machine learning.
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