Instructor
Teaching Assistant
Teaching Assistant
Teaching Assistant
Linear Algebra and Matrix Analysis
Multivariate Calculus
Basic Probability and Statistics
Optimization
| Session | Topic | Videos | Slides | Labs | Assignments |
|---|---|---|---|---|---|
| Session1 | Introduction and Logistics | Aparat, Youtube | lecture1 | Lab0, Lab1 Lab1-files | |
| Session2 | Vectors, Vector Space, Span, Basis, Coordinates | lecture2 | |||
| Session3 | Column space , Row space , Linear Subspace | lecture3 | |||
| Session4 | linear maps, matrix multiplication, matrix rank | lecture4 | |||
| Session5 | Linear Equations, Singular and Non-singular matrices | lecture5 | Lab2, Lab2-files | ||
| Session6 | null space, solution to general linear equations | lecture6 | Home work1 | ||
| Session7 | Homogeneous Equations, Vector Norm, Orthogonality | lecture7 | Lab3, Lab3-files | ||
| Session8 | rthonormal Basis, QR decomposition, least squares | lecture8 | |||
| Session9 | Determinant, Intro to Eigenvalues and Eigenvectors | lecture9 | |||
| Session10 | Eigenvalues and Eigenvectors, Algebraic Multiplicity | lecture10 | |||
| Session11 | Complex Matrices, Diagonalization | lecture11 | Home work2 | ||
| Session12 | Properties of Positive Definite Matrices | lecture12 | |||
| Session13 | SVD, Matrix Norm, Low-rank approximation | lecture13 | |||
| Session14 | Noisy Homogeneous Equations, Affine spaces | ||||
| Session15 | Derivative of dot product | lecture15 | Home work3 | ||
| Session16 | The gradient vector | ||||
| Session17 | functions of matrices, linear regression | lecture17 | |||
| Session18 | Jacobian Matrix, Chain Rule, Automatic Differentiation | lecture18 | |||
| Session19 | Second Derivatives, Multilinear maps and Tensors, the Hessian Matrix | lecture19 | |||
| Session20 | Modeling Uncertainty, Random Variables, Probability mass | lecture20 | Lab4, Lab4-files | ||
| Session21 | Joint distribution, Marginal Distribution, Conditional Distribution | lecture21 | |||
| Session22 | Probabilistic Independence | lecture22 | Lab5, Lab5-files | ||
| Session23 | Statistical Measures: mean, median, mode, variance, standard deviation, covariance matrix | lecture23 | |||
| Session24 | Common Probability Distributions | lecture24 | Home work4 | ||
| Session25 | Linear Equations, Singular and Non-singular matrices | lecture25 | |||
| Session26 | Sampling | lecture26 | |||
| Session27 | Statistical Estimation, Maximum Likelihood Solution, Introduction to Optimization | lecture27 | |||
| Session28 | Gradient Descent, SGD, Momentum, Quadratic Approximation, Newton's method | lecture28 |
