Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

contents

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

수학 개인 공부 1장은 건너뜁니다!