Video Recordings for Previous Semesters

Lecture	Lecture content	Fall 22 Recording	Spring 23 Recording
1	Introduction, what is optimization? Least-squares, Minimum norm	Lecture 1	Lecture 1
2	Least squares review: Vector norms, Gram Schmidt-QR, Fundamental Theorem of Linear Algebra	Lecture 2	Lecture 2
3	Symmetric Matrices + Eigenvalues + Rayleigh coef + Power Iteration, Matrix Norms, Matrix Square Root, PSD Matrices	Lecture 3	Lecture 3
4	SVD and PCA	Lecture 4	Lecture 4
5	Low-rank approximation — Eckert-Young theorem. Matrix Norms	Lecture 5	Lecture 5
6	Low-rank approximation — Eckert-Young theorem part 2	Lecture 6	Lecture 6
7	Vector Calculus	Lecture 7	Lecture 7
8	Ridge regression: 3 interpretations. (1) Ill-conditioned matrices (2) Modified Least squares (3) Ghost Data (connection to Tikhonov)	Lecture 8	Lecture 8
9	PCA and ridge connection. Least-Squares MLE connection, Ridge: MAP connection.	Lecture 9	Lecture 9
10	Convexity	Lecture 10	Lecture 10
11	Convexity	Lecture 11	Lecture 11
12	Gradient descent + convergence	Lecture 12	Lecture 12
13	SGD, Projected Gradient Descent, Frank Wolfe	Lecture 13	Lecture 13 Lecture 14 Lecture 15
14	Midterm review	MT Review
15	Convex Optimization
16	Weak duality		Lecture 16
17	Strong duality		Lecture 17
18	Optimality conditions, KKT		Lecture 18
19	LPs		Lecture 19 Lecture 20
20	QPs		Lecture 21
21	SOCPs		Lecture 22