Mathematics for Machine Learning (Record no. 47571)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02135cam a22002658i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | RNL |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260107094444.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 191130s2020 enk b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781108470049 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781108455145 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | RCL |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 D27M |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Deisenroth, Marc Peter, |
| 245 10 - TITLE STATEMENT | |
| Title | Mathematics for Machine Learning |
| Statement of responsibility, etc | /Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication | New York: |
| Name of publisher | Cambridge University Press, |
| Year of publication | 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xvii, 371p. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Introduction and motivation -- Linear algebra -- Analytic geometry -- Matrix decompositions -- Vector calculus -- Probability and distribution -- Continuous optimization -- When models meet data -- Linear regression -- Dimensionality reduction with principal component analysis -- Density estimation with Gaussian mixture models -- Classification with support vector machines. |
| 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"-- |
| 546 ## - LANGUAGE NOTE | |
| Language note | English |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Faisal, A. Aldo, |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ong, Cheng Soon, |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Full call number | Accession Number | Lost status | Damaged status | Price effective from | Koha item type | Not for loan | Collection code | Withdrawn status | Home library | Current library | Shelving location | Date acquired | Cost, normal purchase price |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 006.31 D27M | 65339 | 12/11/2025 | Books | Mathematics Department Books | RCL | RCL | General Stacks | 11/18/2025 | 4662.00 | ||||
| 006.31 D27M | 65338 | 12/11/2025 | Books | Mathematics Department Books | RCL | RCL | General Stacks | 11/18/2025 | 4662.00 |