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 
1252016  Overview of the course  Slides 1  Notes 1  
212016  Convex optimization Convex sets and functions, duality, optimality conditions  Slides 2  Notes 2  
282016  Optimization algorithms Gradient descent, subgradient method, proximal methods, coordinate descent  Slides 3  Notes 3  
2152016  No class (University holiday)    Homework 1 Pb 2 (script, additional files: 1, 2, 3) 
2222016  Sparse models and denoising Frequency representations, wavelets, pursuit methods, thresholding, total variation  Slides 4  Notes 4  Project proposal due 
2292016  Random projections Dimensionality reduction, compressed sensing  Slides 5  Notes 5  
372016  Random projections (continued)    
3142016  No class (Spring break)    
3212016  Superresolution Prony's method, subspace methods, optimizationbased superresolution  Slides 6  Notes 6  
3282016  Sparse regression Linear regression, the lasso, the elastic net, the group lasso  Slides 7  Notes 7  
442016  Sparse regression (continued)    Homework 2 (Pb 1, Pb 2) 
4112016  Learning representations K means, PCA, nonnegative matrix fact., sparse PCA, dictionary learning  Slides 8  Notes 8  Homework 1 due 
4252016  Learning representations (continued)    
522016  Lowrank models Matrix completion, robust PCA  Slides 9  Notes 9  
592016  Review of main ideas  Slides 10  No notes  Homework 2 due Project report is due on May 12

