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 
962017  Overview of the course + Vector spaces Linear algebra, nearestneighbor classification, projections, denoising  Vector spaces  Vector spaces  
9132017  Matrices Linear maps, singularvalue decomposition, principalcomponent analysis, eigendecomposition  Matrices  Matrices  
9202017  Matrices (continued)    Hw 1 due 
9272017  Randomness Gaussian vectors, random projections, randomized svd  Randomness  Randomness  Project proposal due 
1042017  The Fourier domain Frequency representation, convolutions, filtering, sampling theorem, superresolution  Fourier  Fourier  Hw 2 due 
10112017  The Fourier domain (continued)  Spectral SR   
10182017  Multiresolution Shorttime Fourier transform, wavelets, thresholding  Multiresolution  Multiresolution  
10252017  Linear models Linear regression, overfitting regularization, logistic regression  Linear models  Linear models  Hw 3 due 
1182017  Linear models (continued)    Hw 4 due 
11152017  Convex optimization Convexity, differentiable functions, optimization algorithms  Convex optimization  Convex optimization  
11292017  Nondifferentiable functions Sparse regression, robust PCA, subgradients, algorithms  Nondifferentiable functions  Nondifferentiable functions  Hw 5 due 
1262017  Constrained optimization Duality, dual certificates, compressed sensing, matrix completion  Constrained optimization  Constrained optimization  
12132017  Matrix factorization Matrix completion, nonnegative matrix factorization, Sparse PCA  Matrix factorization  Matrix factorization  Final project due on December 15

