| 000 | 02334nam a22002657a 4500 | ||
|---|---|---|---|
| 003 | UAHC | ||
| 005 | 20250627135856.0 | ||
| 008 | 250627s2020 -uk|||| |||| 00| 0 eng d | ||
| 020 | _a9781108455145 | ||
| 040 |
_aUAHC _cUAHC |
||
| 082 |
_a006.31 _bD325 |
||
| 100 |
_aDeisenroth, Marc Peter _921745 |
||
| 245 | 1 | 0 |
_aMathematics for machine learning _cMarc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong. |
| 260 |
_aCambridge : _bCambridge University Press, _c2020. |
||
| 300 | _a371 páginas. | ||
| 505 | _a1. Introduction and motivation -- 2. Linear algebra -- 3. Analytic geometry -- 4. Matrix decompositions -- 5. Vector calculus -- 6. Probability and distribution -- 7. Optimization -- 8. When models meet data -- 9. Linear regression -- 10. Dimensionality reduction with principal component analysis -- 11. Density estimation with Gaussian mixture models -- 12. Classification with support vector machines. | ||
| 520 | _aThe 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. | ||
| 650 | 0 |
_aAprendizaje automático _921744 |
|
| 650 | 0 |
_aAprendizaje automático _xMatemáticas _921748 |
|
| 650 | 0 |
_aMatemáticas _9535 |
|
| 700 |
_aFaisal, A. Aldo _921746 _eautor |
||
| 700 |
_aOng, Cheng Soon _921747 _eautor |
||
| 900 | _a006.31 DEI | ||
| 942 |
_2ddc _cBK |
||
| 999 |
_c62718 _d62718 |
||