Week 1 : Vectors in Machine Learning, Basics of Matrix Algebra,Vector Space, Subspace, Basis and Dimension.
Week 2 : Linear Transformations, Norms and Spaces, Orthogonal Complement and Projection Mapping, Eigenvalues and Eigenvectors, Special Matrices and Properties.
Week 3 : Spectral Decomposition, Singular Value Decomposition,Low Rank Approximations, Python Implementation of SVD and Low-rank Approximation.
Week 4 : Principal Component Analysis,Python Implementation of PCA, Linear Discriminant Analysis, Python Implementation of LDA.
Week 5 : Least Square Approximation and Minimum Normed Solution, Linear and Multiple Regression, Logistic Regression.
Week 6 : Classification Metrics, Gram Schmidt Process, Polar Decomposition, Minimal Polynomial and Jordan Canonical Form, Some more Matrices Applications in Machine Leaning.
Week 7 : Basics concepts of Calculus, gradient, Jacobian, Chain rule,Change of variables.
Week 8 : Calculus in Python, Convex sets and convex functions, properties of convex functions, Introduction to Optimization.
Week 9 : Numerical Optimization in Machine Learning, Gradient Descent and other optimization algorithms in machine learning.
Week 10 : Optimization using Python, Review of Probability, Bayes theorem and random variable, Expectation and variance.
Week 11 : Discrete and continuous distribution functions, joint probability and covariance, Introduction to SVM, Error minimizing LPP.
Week 12 : Lagrangian Multiplier method, concepts of duality, hard and soft margin classifier, SVM in Python.