000			03368nam a2200217 4500
005			20250507165852.0
008			250507b |||||||| |||| 00| 0 eng d
020			_a9781108455145
082			_a006.31 _bDEI
100			_aDeisenroth, Marc Peter _923992
245			_aMathematics for machine learning
260			_bCambridge University Press _aNew York _c2020
300			_axvii, 371.p
365			_aGBP _b39.99
500			_aTable 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]
520			_aThe 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)
650			_aMachine learning
700			_aFaisal, Aldo A _923993
700			_aOng, Cheng Soon _923994
942			_cBK _2ddc
999			_c9674 _d9674