Mathematics for Machine Learning – Deisenroth & Faisal Paperback |9781108455145|

Mathematics for Machine Learning provides the indispensable mathematical foundations required to understand and build advanced artificial intelligence systems today. Mathematics for Machine Learning bridges the gap between mathematical theory and the practical application of algorithms in data science. Instead of viewing these topics as isolated subjects, authors Deisenroth, Faisal, and Ong present them as a unified toolkit for the modern engineer.

About the Book

Mathematics for Machine Learning is designed to be the definitive entry point for anyone looking to go beyond simply using libraries like Scikit-Learn or TensorFlow. This textbook covers the core mathematical pillars—linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics—that underpin almost every machine learning model. By mastering these concepts, readers gain the ability to troubleshoot models and innovate new solutions in the field of AI.

What You’ll Learn / Why Read

Mathematics for Machine Learning helps you master the "why" behind the algorithms. You will learn how linear algebra defines high-dimensional data spaces and how vector calculus drives the optimization process in neural networks. Furthermore, the text explains how probability distributions model uncertainty and how principal component analysis (PCA) reduces data complexity. This book is essential for transitioning from a "user" of AI to a "creator" of robust machine learning systems.

Author Bio

Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong are world-renowned researchers and educators. Their combined expertise in robotics, computational neuroscience, and statistical machine learning ensures that this text is both academically rigorous and practically relevant to the current industry landscape.

Product Details

• Author: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

• Publisher: Cambridge University Press

• Language: English

• Format: Paperback / Softcover

• ISBN-13: 978-1108455145

• Genre: Mathematics / Data Science / AI

• Pages: 398 Pages

Why Buy from Nybookshub

Mathematics for Machine Learning customers trust Nybookshub for our commitment to quality and academic excellence. We provide 100% authentic editions, ensuring you receive the high-fidelity diagrams and formulas necessary for technical study. Our global shipping network ensures that this vital AI resource reaches aspiring data scientists in every corner of the globe. At Nybookshub, we curate a collection specifically designed to empower the next generation of tech innovators.

Questions & Answers

Does this book cover deep learning math? Yes, it covers the foundational calculus and optimization required to understand backpropagation and neural network training.

Is this the authentic Cambridge University Press edition? Absolutely. Nybookshub only stocks verified, original editions for our global community of readers.

Who is the target audience for this text? It is perfect for upper-level undergraduates, graduate students, and software engineers looking to pivot into Data Science.

Does it include practical programming examples? While focused on math, it provides the intuition needed to implement algorithms in languages like Python or R.

Are there exercises included in the book? Yes, it features various exercises designed to test your understanding of complex mathematical concepts.

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