Mathematics for machine learning (Record no. 9674)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03368nam a2200217 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250507165852.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250507b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781108455145 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | DEI |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Deisenroth, Marc Peter |
| 245 ## - TITLE STATEMENT | |
| Title | Mathematics for machine learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Cambridge University Press |
| Place of publication, distribution, etc. | New York |
| Date of publication, distribution, etc. | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xvii, 371.p |
| 365 ## - TRADE PRICE | |
| Price type code | GBP |
| Price amount | 39.99 |
| 500 ## - GENERAL NOTE | |
| General note | Table of contents:<br/>Part I - Mathematical Foundations<br/><br/>pp 1-2<br/>1 - Introduction and Motivation<br/><br/>pp 3-7<br/>2 - Linear Algebra<br/><br/>pp 8-56<br/>3 - Analytic Geometry<br/><br/>pp 57-81<br/>4 - Matrix Decompositions<br/><br/>pp 82-119<br/>5 - Vector Calculus<br/><br/>pp 120-151<br/>6 - Probability and Distributions<br/><br/>pp 152-200<br/>7 - Continuous Optimization<br/><br/>pp 201-222<br/>Part II - Central Machine Learning Problems<br/><br/>pp 223-224<br/>8 - When Models Meet Data<br/><br/>pp 225-259<br/>9 - Linear Regression<br/><br/>pp 260-285<br/>10 - Dimensionality Reduction with Principal Component Analysis<br/><br/>pp 286-313<br/>11 - Density Estimation with Gaussian Mixture Models<br/><br/>pp 314-334<br/>12 - Classification with Support Vector Machines<br/><br/>pp 335-356<br/>References<br/><br/>pp 357-366<br/>Index<br/><br/>pp 367-372<br/><br/>[Part I - Mathematical Foundations<br/><br/>pp 1-2<br/>1 - Introduction and Motivation<br/><br/>pp 3-7<br/>2 - Linear Algebra<br/><br/>pp 8-56<br/>3 - Analytic Geometry<br/><br/>pp 57-81<br/>4 - Matrix Decompositions<br/><br/>pp 82-119<br/>5 - Vector Calculus<br/><br/>pp 120-151<br/>6 - Probability and Distributions<br/><br/>pp 152-200<br/>7 - Continuous Optimization<br/><br/>pp 201-222<br/>Part II - Central Machine Learning Problems<br/><br/>pp 223-224<br/>8 - When Models Meet Data<br/><br/>pp 225-259<br/>9 - Linear Regression<br/><br/>pp 260-285<br/>10 - Dimensionality Reduction with Principal Component Analysis<br/><br/>pp 286-313<br/>11 - Density Estimation with Gaussian Mixture Models<br/><br/>pp 314-334<br/>12 - Classification with Support Vector Machines<br/><br/>pp 335-356<br/>References<br/><br/>[https://www.cambridge.org/highereducation/books/mathematics-for-machine-learning/5EE57FD1CFB23E6EB11E130309C7EF98#contents] |
| 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. Programming tutorials are offered on the book's web site.<br/><br/>(https://www.cambridge.org/highereducation/books/mathematics-for-machine-learning/5EE57FD1CFB23E6EB11E130309C7EF98#contents) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Faisal, Aldo A |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ong, Cheng Soon |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Bill No | Bill Date | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Accession Number | Date last seen | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | TB4762 | 21-03-2025 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 03/28/2025 | Technical Bureau India Pvt. Ltd. | 2893.08 | 006.31 DEI | 008601 | 03/28/2025 | 4450.89 | 03/28/2025 | Book |