Week  Topics  Study Materials  Materials 
1 
Introducing the Course and Computer tools



2 
Basic Concepts of Linear Algebra and its computer implementations in Python



3 
Linear mappings and affine transformations



4 
Norms, angles, orthogonality and orthogonal projection in Python



5 
Matrix Decompositions and its computer implementations



6 
Computing eigenvalues, eigenvectors, diagonalization of matrices and singular value decomposition in Python



7 
Vector Calculus and its computer implementations in Python



8 
Construction of a probability space, Discrete and continuous probabilities, and Bayes’ theorem with computer applications



9 
Summary of statistics and Gaussian distribution in Python



10 
Unconstrained and constrained optimization with applications



11 
Convex optimization with applications



12 
Data, features and model selection



13 
Simple linear regression and Maximum likelihood estimation with computer applications



14 
Dimensionality Reduction with Principal Component Analysis in Python



15 
Final



16 
Final


