Mathematics for Machine Learning

Mathematics for Machine Learning by Marc Peter Deisenroth

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

'This book provides great coverage of all the basic mathematical concepts for machine learningI'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' Joelle Pineau, McGill University, Montreal
'The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field, and as a reference text for machine learning researchers and engineers.' Christopher Bishop, Microsoft Research Cambridge
'This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.' Pieter Abbeel, University of California, Berkeley
'Really successful are the numerous explanatory illustrations, which help to explain even difficult concepts in a catchy way. Each chapter concludes with many instructive exercises. An outstanding feature of this book is the additional material presented on the website …' Volker H. Schulz, SIAM Review

Faisal, A. Aldo: - A. Aldo Faisal leads the Brain and Behaviour Lab at Imperial College London, where he is faculty at the Departments of Bioengineering and Computing and a Fellow of the Data Science Institute. He is the director of the 20Mio� UKRI Center for Doctoral Training in AI for Healthcare. Faisal studied Computer Science and Physics at the Universit�t Bielefeld (Germany). He obtained a Ph.D. in Computational Neuroscience at the University of Cambridge and became Junior Research Fellow in the Computational and Biological Learning Lab. His research is at the interface of neuroscience and machine learning to understand and reverse engineer brains and behavior.

SKU	Unavailable
ISBN 13	9781108455145
ISBN 10	110845514X
Title	Mathematics for Machine Learning
Author	Marc Peter Deisenroth
Condition	Unavailable
Binding Type	Paperback
Publisher	Cambridge University Press
Year published	2020-04-23
Number of pages	398
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	Unavailable