Schedule

Here is a tentative schedule for the course, the contents are subject to modifications.

Additional references are listed at the end of the notes.

Date	Contents	Slides	Notes	Deadlines
1-25-2016	Overview of the course	Slides 1	Notes 1
2-1-2016	Convex optimization Convex sets and functions, duality, optimality conditions	Slides 2	Notes 2
2-8-2016	Optimization algorithms Gradient descent, subgradient method, proximal methods, coordinate descent	Slides 3	Notes 3
2-15-2016	No class (University holiday)			Homework 1 Pb 2 (script, additional files: 1, 2, 3)
2-22-2016	Sparse models and denoising Frequency representations, wavelets, pursuit methods, thresholding, total variation	Slides 4	Notes 4	Project proposal due
2-29-2016	Random projections Dimensionality reduction, compressed sensing	Slides 5	Notes 5
3-7-2016	Random projections (continued)
3-14-2016	No class (Spring break)
3-21-2016	Super-resolution Prony's method, subspace methods, optimization-based super-resolution	Slides 6	Notes 6
3-28-2016	Sparse regression Linear regression, the lasso, the elastic net, the group lasso	Slides 7	Notes 7
4-4-2016	Sparse regression (continued)			Homework 2 (Pb 1, Pb 2)
4-11-2016	Learning representations K means, PCA, nonnegative matrix fact., sparse PCA, dictionary learning	Slides 8	Notes 8	Homework 1 due
4-25-2016	Learning representations (continued)
5-2-2016	Low-rank models Matrix completion, robust PCA	Slides 9	Notes 9
5-9-2016	Review of main ideas	Slides 10	No notes	Homework 2 due Project report is due on May 12