Mathematics for machine learning
Deisenroth, Marc Peter
Mathematics for machine learning - New York Cambridge University Press 2020 - xvii, 371.p
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)
9781108455145
Machine learning
006.31 / DEI
Mathematics for machine learning - New York Cambridge University Press 2020 - xvii, 371.p
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)
9781108455145
Machine learning
006.31 / DEI