| 000 -LEADER |
|---|
| fixed length control field |
02300 a2200289 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20250904141111.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250904b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| ISBN |
9781108455145 |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 DEI/M |
| 100 ## - MAIN ENTRY--AUTHOR NAME |
|---|
| Personal name |
Deisenroth, Marc Peter |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Mathematics for machine learning |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
1st/ 2020 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher |
Cambridge University Press |
| Place of publication |
Cambridge |
| Year of publication |
c2020 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Number of Pages |
xvii, 371p.; 25cm. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
<br/>Table of Contents<br/><br/>1. Introduction and motivation<br/>2. Linear algebra<br/>3. Analytic geometry<br/>4. Matrix decompositions<br/>5. Vector calculus<br/>6. Probability and distribution<br/>7. Optimization<br/>8. When models meet data<br/>9. Linear regression<br/>10. Dimensionality reduction with principal component analysis<br/>11. Density estimation with Gaussian mixture models<br/>12. Classification with support vector machines.<br/> |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical Term |
Computer science |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical Term |
Pattern recognition |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical Term |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical Term |
Artificial intelligence |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical Term |
Mathematics |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Faisal, A. Aldo |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ong, Cheng Soon |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
https://doi.org/10.1017/9781108679930 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Book |
