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> Mathematics for Machine Learning

Mathematics for Machine Learning

Marc Peter Deisenroth, University College London, A. Aldo Faisal, Imperial College London, Cheng Soon Ong, Data61, CSIRO
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…
pp i-iv
pp v-viii
List of Symbols
pp ix-x
pp xi-xiv
pp xv-xviii
Part I - Mathematical Foundations
pp 1-2
1 - Introduction and Motivation
pp 3-7
2 - Linear Algebra
pp 8-56
3 - Analytic Geometry
pp 57-81
4 - Matrix Decompositions
pp 82-119
5 - Vector Calculus
pp 120-151
6 - Probability and Distributions
pp 152-200
7 - Continuous Optimization
pp 201-222
Part II - Central Machine Learning Problems
pp 223-224
8 - When Models Meet Data
pp 225-259
9 - Linear Regression
pp 260-285
10 - Dimensionality Reduction with Principal Component Analysis
pp 286-313
11 - Density Estimation with Gaussian Mixture Models
pp 314-334
12 - Classification with Support Vector Machines
pp 335-356
pp 357-366
pp 367-372
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Online ISBN: 9781108679930
Published online: 20 February 2020
Hardback ISBN: 9781108470049
Hardback publication date: 23 April 2020
Paperback ISBN: 9781108455145
Paperback publication date: 23 April 2020