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 Title Mathematics for Machine Learning
 Author(s) Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
 Publisher: Cambridge University Press; 1st edition (April 23, 2020); eBook (GitHub Edition)
 Permission: This version is free to view and download for personal use only. Not for redistribution, resale, or use in derivative works.
 Paperback: 398 pages
 eBook: PDF (412 pages)
 Language: English
 ISBN10/ASIN: 110845514X
 ISBN13: 9781108455145
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Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites.
About the Author(s) Marc Peter Deisenroth is DeepMind Chair in Artificial Intelligence at the Department of Computer Science, University College London.
 Machine Learning
 Algebra, Abstract and Linear Algebra, etc.
 Calculus and Mathematical Analysis
 Statistics
 Probability and Stochastic Processes
 Mathematics for Computer Science

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