TY  - BOOK
AU  - Deisenroth, Marc Peter
AU  - Faisal, A. Aldo
AU  - Ong, Cheng Soon
TI  - Mathematics for machine learning
SN  - 9781108455145
U1  - 006.31 D45 
PY  - 2021///
CY  - New York
PB  - Cambridge University Press
KW  - Machine learning
KW  - Mathematics
KW  - Linear algebra and analytic geometry
KW  - Matrix decompositions and vector calculus
N1  - 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.

A one-stop presentation of all the mathematical background needed for machine learning
Worked examples make it easier to understand the theory and build both practical experience and intuition
Explains central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines
ER  -