| No | Chapter | Link |
|---|---|---|
| Part 1 | Mathematical Foundation | |
| 01 | Introduction and Motivation | M4ML CH 01 |
| 02 | Linear Algebra | M4ML CH 02 |
| 03 | Analytic Geometry | M4ML CH 03 |
| 04 | Matrix Decompositions | M4ML CH 04 |
| 05 | Vector Calculus | M4ML CH 05 |
| 06 | Probability and Distributions | M4ML CH 06 |
| 07 | Continuous Optimization | M4ML CH 07 |
| Part 2 | Central Machine Learning Problems | |
| 08 | When Models Meet Data | M4ML CH 08 |
| 09 | Linear Regression | M4ML CH 09 |
| 10 | Dimensionality Reduction with Principal Component Analysis | M4ML CH 10 |
| 11 | Density Estimation with Gaussian Mixture Models | M4ML CH 11 |
| 12 | Classification with Support Vector Machines | M4ML CH 12 |
Reference
고려대학교 교수님께서 강의자료를 올려놓으신 사이트
Mathematics for Machine Learning – SaVAnNA Lab
10, 11, 12단원이 없어서 아쉽다.
Mathematics For Machine Learning 스터디 노트
Mathematics For Machine Learning 스터디 노트
마찬가지로 뒷부분이 없다.