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
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