Mathematics for machine learning
- Cambridge, United Kingdom: Cambridge University Press, 2020
- xvii, 371 páginas: ilustraciones (algunas a color)
Mathematical Foundations. Introduction and motivation -- Linear algebra -- Analytic geometry -- Matrix decompositions -- Vector calculus -- Probability and distribution -- Continuous optimization -- Central Machine Learning Problems. When models meet data -- Linear regression -- Dimensionality reduction with principal component analysis -- Density estimation with Gaussian mixture models -- Classification with support vector machines.