45:28

Feducation Series, Purpose Limitation At Scale with Privacy Aware Infrastructure, Amritha Raghunath

57:55

Feducation Series, Kernel-Based Reinforcement Learning, By Sattar Vakili

43:39

Tilted Losses in Machine Learning: Theory and Applications to Federated Learning, By Tian Li

39:40

Feducation Series, Learning Situation Hyper-Graphs for Video Question Answering, By Aisha Urooj

56:45

Feducation Series, Privacy Principles and Technologies for Large Foundation Models, by Peter Kairouz

46:28

Feducation Series, Artificial Intelligence and Wireless Systems: A Closer Union, by Walid Saad

49:34

Feducation Series, Federated Learning with Dynamic Resource Availability, by Shiqiang Wang

14:35

Gradients (Module3, Part 1) Introduction to Linear Algebra for Computer Science

14:50

Gradients (Module3, Part 2) Introduction to Linear Algebra for Computer Science

18:27

Clustering (k-means) (Module4, Part 1) Introduction to Linear Algebra for Computer Science

14:12

Clustering (k-means) (Module4, Part 2) Introduction to Linear Algebra for Computer Science

16:30

Regression (Univariate) (Module5, Part 1) Introduction to Linear Algebra for Computer Science

16:31

Regression (Multivariate) (Module5, Part 2) Introduction to Linear Algebra for Computer Science

27:13

Eigenvalue and Eigenvector (Module6, Part 1) Introduction to Linear Algebra for Computer Science

22:59

LU Decomposition (Module6, Part 2) Introduction to Linear Algebra for Computer Science

17:33

QR Decomposition (Module6, Part 3) Introduction to Linear Algebra for Computer Science

12:10

Eigendecomposition and SVD (Module6, Part 4) Introduction to Linear Algebra for Computer Science

39:25

Linear Programming (Optional Module, Part 1) Introduction to Linear Algebra for Computer Science

22:44

Linear Programming (Optional Module, Part 2) Introduction to Linear Algebra for Computer Science

22:50

Linear Programming (Optional Module, Part 3) Introduction to Linear Algebra for Computer Science

19:51

Matrices (Module2, Part 5) Introduction to Linear Algebra for Computer Science

19:40

Matrices (Module2, Part 4) Introduction to Linear Algebra for Computer Science

17:32

Matrices (Module2, Part 3) Introduction to Linear Algebra for Computer Science

32:04

Matrices (Module2, Part 2) Introduction to Linear Algebra for Computer Science

18:26

Matrices (Module2, Part 1) Introduction to Linear Algebra for Computer Science

10:56

Vectors (Module1, A note on linear dependence) Introduction to Linear Algebra for Computer Science

14:43

Vectors (Module1, Supplementary Lecture) Introduction to Linear Algebra for Computer Science

08:33

Vectors (Module1, Part 5) Introduction to Linear Algebra for Computer Science

19:28

Vectors (Module1, Part 3) Introduction to Linear Algebra for Computer Science

22:26

Vectors (Module1, Part 2) Introduction to Linear Algebra for Computer Science

11:49

Vectors (Module1, Part 4) Introduction to Linear Algebra for Computer Science

19:52

Vectors (Module1, Part 1) Introduction to Linear Algebra for Computer Science

37:47

Lecture 31: Decomposition for Nonlinear Optimization (Lagrangian Relaxation Decomposition)

25:43

Lecture 30: Decomposition for Linear Optimization (Part 2: Dantzig-Wolfe Decomposition)

25:49

Lecture 29: Decomposition for Linear Optimization (Part 1: Introduction)

23:37

Lecture 28: Duality in Nonlinear Optimization

25:57

Lecture 25: Duality for Linear Programming (Part 1)

12:46

Lecture 27: Duality for Linear Programming (Part 3: Example)

11:30

Lecture 26: Duality for Linear Programming (Part 2)

12:26

Lecture 24: Unlimited Point Algorithm

20:45

Lecture 23: Interior Point Method

22:53

Lecture 17: Linear Optimization ( Part 2: Step-by-Step Simplex Algorithm)

17:56

Lecture 22: Nonlinear Optimization (Part 4: Quadratic Optimization)

12:40

Lecture 21: Nonlinear Optimization (Part 3: Gradient Descent and Steepest Descent Algorithms)

13:45

Lecture 20: Nonlinear Optimization (Part 2: Example of Newton-Raphson Method)

25:50

Lecture 19: Nonlinear Optimization (Part 1: Newton-Raphson Method)

22:50

Lecture 18: Linear Optimization (Part 3: An Example of Simplex Algorithm)

39:25

Lecture 16: Linear Optimization (Part 1: Introduction to Simplex Algorithm and Standard Tableau)

19:23

Lecture 15: Examples of Unconstrained, Equality/Inequality Constrained Optimization Problems

20:19

Lecture 14: Inequality Constrained Optimization

16:30

Lecture 13: Introduction to Linear Algebra (Part 7)

12:55

Lecture 12: Introduction to Linear Algebra (Part 6)

17:56

Lecture 11: Introduction to Linear Algebra (Part 5)

28:29

Lecture 10: Introduction to Linear Algebra (Part 4)

19:24

Lecture 9: Introduction to Linear Algebra (Part 3)

23:53

Lecture 8: Introduction to Linear Algebra (Part 2)

30:30

Lecture 7: Introduction to Linear Algebra (Part 1)

21:33

Lecture 6: Equality Constrained Optimization

18:11

Lecture 5: Unconstrained Optimization (Part 2: Convexity)

20:07

Lecture 4: Unconstrained Optimization (Part 1)