MATHEMATICS FOR
MACHINE LEARNING
Marc Peter Deisenroth
A. Aldo Faisal
Cheng Soon Ong
MATHEMATICS
FOR
MACHINE LEARNING
DEISENROTH ET AL.
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 effi ciently 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 fi rst 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.
MARC PETER DEISENROTH is Senior Lecturer in Statistical Machine
Learning at the Department of Computing, Împerial College London.
A. ALDO FAISAL leads the Brain & Behaviour Lab at Imperial College
London, where he is also Reader in Neurotechnology at the Department of
Bioengineering and the Department of Computing.
CHENG SOON ONG is Principal Research Scientist at the Machine Learning
Research Group, Data61, CSIRO. He is also Adjunct Associate Professor at
Australian National University.
Cover image courtesy of Daniel Bosma / Moment / Getty Images
Cover design by Holly Johnson
Deisenrith et al. 9781108455145 Cover. C M Y K
