MLF Weekwise Summary

Week	Topics
Week 3	The Four Fundamental Subspaces Projections Ax = b : Least Square Solution
Week 4	Linear and Polynomial Regression Eigenvalues and Eigenvectors Diagonalization
Week 5	Complex Numbers and Complex Vectors Hermitian and Unitary Matrices
Week 6	Singular Value Decomposition Positive Definite Matrices
Week 7	Principal Component Analysis
Week 8	Introduction to Optimization Unconstrained Optimization
Week 9	Constrained Optimization: One Inequality Constrained Optimization: One Equality Convex Sets Convex Functions Properties of Convex Functions
Week 10	Optimization: An Application in ML Revisiting Optimization Karush-Kuhn-Tucker (KKT) Conditions
Week 11	Continuous Random Variables Two Random Variables
Week 12	Random Vectors Bivariate and Multivariate Normal Maximum Likelihood Estimation Linear Regression with Gaussian Noise Gaussian Mixture Model Some Inequalities and CLT