MARC details
| 000 -LEADER |
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| fixed length control field |
02011 a2200241 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230223b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781108455145 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
DEI |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Deisenroth, Marc Peter |
| 245 ## - TITLE STATEMENT |
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| Title |
Mathematics for machine learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Cambridge University Press, |
| Date of publication, distribution, etc |
2020. |
| Place of publication, distribution, etc |
Cambridge: |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xvii, 371p.: |
| Other physical details |
col. ill.; pbk: |
| Dimensions |
25cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Include References and index |
| 520 ## - SUMMARY, ETC. |
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| 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/><br/><br/>https://www.cambridge.org/highereducation/books/mathematics-for-machine-learning/5EE57FD1CFB23E6EB11E130309C7EF98#overview |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning Mathematics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Vector calcus |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Linear regression |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data, models and learning |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Faisal, A. Aldo |
| Relator term |
Co-author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Ong, Cheng Soon |
| Relator term |
Co-author |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Materials specified |
https://mml-book.github.io/book/mml-book.pdf |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Item type |
Books |
