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