Mathematics for Machine Learning, (Paperback)

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Key item features

Essential Mathematics: Covers fundamental tools for machine learning: linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics.
Accessible Textbook: Bridges the gap between mathematical and machine learning texts, introducing concepts with minimal prerequisites for efficient learning.
Practical ML Methods: Uses concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines.
Dual Learning Paths: Provides starting points for those with a math background and builds intuition for first-time learners through practical concept application.
Supportive Resources: Includes worked examples and exercises in each chapter; programming tutorials are available on the book's website.
Publication Details: This paperback by Marc Peter Deisenroth (ISBN 9781108455145), published April 23, 2020, with 398 pages.

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