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 compute...
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| Formato: | Libro |
| Lenguaje: | inglés |
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Cambridge :
Cambridge University Press,
2020.
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Tabla de Contenidos:
- 1. 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.