Mathematics of Machine Learning

Table of Contents

1. Vectors and vector spaces
2. The geometric structure of vector spaces
3. Linear algebra in practice spaces: measuring distances
4. Linear transformations
5. Matrices and equations
6. Eigenvalues and eigenvectors
7. Matrix factorizations
8. Matrices and graphs
9. Functions
10. Numbers, sequences, and series
11. Topology, limits, and continuity
12. Differentiation
13. Optimization
14. Integration
15. Multivariable functions
16. Derivatives and gradients
17. Optimization in multiple variables
18. What is probability?
19. Random variables and distributions
20. The expected value
21. The maximum likelihood estimation
22. It's just logic
23. The structure of mathematics
24. Basics of set theory
25. Complex numbers