Mathematics for machine learning

By:

Deisenroth, Marc Peter

Contributor(s):

Material type: Text

TextPublication details: Cambridge University Press New York 2020Description: xvii, 371.pISBN:

9781108455145

Subject(s):

Machine learning

DDC classification:

006.31 DEI

Summary: 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. (https://www.cambridge.org/highereducation/books/mathematics-for-machine-learning/5EE57FD1CFB23E6EB11E130309C7EF98#contents)

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Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.31 DEI (Browse shelf(Opens below))	Available		008601

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Table of contents:
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
References

pp 357-366
Index

pp 367-372

[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
References

[https://www.cambridge.org/highereducation/books/mathematics-for-machine-learning/5EE57FD1CFB23E6EB11E130309C7EF98#contents]

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.

(https://www.cambridge.org/highereducation/books/mathematics-for-machine-learning/5EE57FD1CFB23E6EB11E130309C7EF98#contents)

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