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
9-6-2017	Overview of the course + Vector spaces Linear algebra, nearest-neighbor classification, projections, denoising	Vector spaces	Vector spaces
9-13-2017	Matrices Linear maps, singular-value decomposition, principal-component analysis, eigendecomposition	Matrices	Matrices
9-20-2017	Matrices (continued)			Hw 1 due
9-27-2017	Randomness Gaussian vectors, random projections, randomized svd	Randomness	Randomness	Project proposal due
10-4-2017	The Fourier domain Frequency representation, convolutions, filtering, sampling theorem, super-resolution	Fourier	Fourier	Hw 2 due
10-11-2017	The Fourier domain (continued)	Spectral SR
10-18-2017	Multiresolution Short-time Fourier transform, wavelets, thresholding	Multiresolution	Multiresolution
10-25-2017	Linear models Linear regression, overfitting regularization, logistic regression	Linear models	Linear models	Hw 3 due
11-8-2017	Linear models (continued)			Hw 4 due
11-15-2017	Convex optimization Convexity, differentiable functions, optimization algorithms	Convex optimization	Convex optimization
11-29-2017	Nondifferentiable functions Sparse regression, robust PCA, subgradients, algorithms	Nondifferentiable functions	Nondifferentiable functions	Hw 5 due
12-6-2017	Constrained optimization Duality, dual certificates, compressed sensing, matrix completion	Constrained optimization	Constrained optimization
12-13-2017	Matrix factorization Matrix completion, nonnegative matrix factorization, Sparse PCA	Matrix factorization	Matrix factorization	Final project due on December 15