# Mathematics for Machine Learning

"Think hard, not work hard." - Prof. R. C. T. Lee

Overview

Course time: 10:10–11:00, Tuesday and 14:10–15:00 Wednesday.
TA: Kuan-Hsun Tsou (鄒冠勲) (Room E814)
Location: E416 (Tuesday) and E509 (Wednesday) at Main Engineering Building, Tamkang University.

Textbooks and other reference material
• Mathematics for Machine Learning. Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Cambridge University Press. 2020. (link to the book)
• Elementary Linear Algebra - Applications Version. 12th Edition. Howard Anton, Chris Rorres, Anton Kaul. 2019.
• Attendance (10%)
• Assignments + Quizzes (30%)
• Midterm (30%)
• Final Exam (30%)
Subjects we plan to cover

*We thank Prof. Deisenroth for permitting us to use the textbook pdf and figures in our lecture slides.
1. Course Introduction [slides]
2. Linear Algebra - Basis, Rank, Linear Mappings & Affine Spaces [slides], [exercise 01]
3. Linear Algebra - Norms, Inner Products & Orthogonality [slides]
4. Linear Algebra - Projections & Gram-Schmidt Orthogonalization [slides]
5. Linear Algebra - Eigenvalues, Eigenvectors, Eigenspaces, Cholesky Decomposition & Diagonalization [slides], [exercise 02]
6. Linear Algebra - Singular Value Decomposition & Matrix Approximation [slides]
7. Vector Calculus - Differentiation, Partial Differentiation & Gradients [slides], [exercise 03]
8. Vector Calculus - Gradients of Vector-Valued Functions and Matrices [slides], [exercise 04]
9. Vector Calculus - Backpropagation & Automatic Differentiation [slides]
10. Vector Calculus - Linearization & Multivariate Taylor Series [slides]
11. Probability and Distributions - Sum Rule, Product Rule, Bayes' Theorem & Summary Statistics [slides]
12. Probability and Distributions - Gaussian Distribution & Change of Variables [slides], [exercise 05]
13. Continuous Optimization - Gradient Descent and Constrained Optimization [slides]
14. Continuous Optimization - Preliminary Convex Optimization [slides]
15. Empirical Risk Minimization [slides]
16. Maximum Likelihood Estimation & Maximum A Posteriori Estimation [slides]
17. Probabilistic Modeling & Inference [slides]
18. Linear Regression - Maximum Likelihood Estimation & Maximum A Posteriori Estimation [slides]
19. Density Estimation with Gaussian Mixture Models [slides]
20. Classification with Support Vector Machines [slides]
Examinations

Any question is welcome.