Mathematics for Machine Learning
Publisher: Cambridge University Press
Published: 2020
Place of publication: Cambridge, United Kingdom
Language: eng
Format: Paperback
Number of pages: 371
ISBN-13: 9781108455145
Copyright date: 2020
Description: "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"--
Table of contents: label: Part I
title: Mathematical Foundations
pagenum: 1; label: Chapter 1
title: Introduction and Motivation
pagenum: 3; label: Chapter 2
title: Linear Algebra
pagenum: 8; label: Chapter 3
title: Analytic Geometry
pagenum: 57; label: Chapter 4
title: Matrix Decompositions
pagenum: 82; label: Chapter 5
title: Vector Calculus
pagenum: 120; label: Chapter 6
title: Probability and Distributions
pagenum: 152; label: Chapter 7
title: Continuous Optimization
pagenum: 201; label: Part II
title: Central Machine Learning Problems
pagenum: 223; label: Chapter 8
title: When Models Meet Data
pagenum: 225; label: Chapter 9
title: Linear Regression
pagenum: 260; label: Chapter 10
title: Dimensionality Reduction with Principal Component Analysis
pagenum: 286; label: Chapter 11
title: Density Estimation with Gaussian Mixture Models
pagenum: 314; label: Chapter 12
title: Classification with Support Vector Machines
pagenum: 335