Mathematics for Machine Learning


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

Textbooks and other reference material
Grading policy 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. Gaussian Mixture Models [slides]
  20. Expectation Maximization [slides]
  21. Classification with Support Vector Machines [slides]
Examinations

Any question is welcome.
Please contact Joseph, Chuang-Chieh Lin (Email to: josephcclin_AT_GMS_TKU_EDU.TW)

© 2004 Joseph Chuang-Chieh Lin