• 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 PDF version is free to view and download for personal use only. Not for re-distribution, re-sale, or use in derivative works.
• Paperback: 398 pages
• eBook: PDF (412 pages)
• Language: English
• ISBN-10/ASIN: 110845514X
• ISBN-13: 978-1108455145
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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.

• Marc Peter Deisenroth is DeepMind Chair in Artificial Intelligence at the Department of Computer Science, University College London.

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• Deep Learning and Neural Networks
• Machine Learning
• Linear and Matrix Algebra
• Applied Mathematics

• Mathematics for Machine Learning (Marc Peter Deisenroth, et al.)
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