Mathematics for machine learning
"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...
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| Otros Autores: | , |
| Formato: | Libro |
| Idioma: | Inglés |
| Publicado: |
Cambridge ; New York, NY :
Cambridge University Press
2020
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| Materias: | |
| Ver en Universidad de Navarra: | https://unika.unav.edu/discovery/fulldisplay?docid=alma991002590999708016&context=L&vid=34UNAV_INST:VU1&search_scope=34UNAV_TODO&tab=34UNAV_TODO&lang=es |
Tabla de Contenidos:
- 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.
