CSE 588: Large-Scale Machine Learning
Course Instructor: Dr. Mehrdad Mahdavi
Topics covered: Probably approximately correct (PAC), Empirical Risk Minimization (ERM), Linear Algebra, Matrix Computation, Convex Analysis, Non-Convex Analysis, Gradient Descent, Stochastic Gradient Descent, Accelerating Gradient Descent, Projection Free Optimization, Non-Convex Optimization, Landscape Analysis, Fast training of DNNs: learning rate schedules and initialization, Accelerated inference for DNNs: model compression, quantization, distillation, compression via pruning, Hyperparameter optimization: naive search methods & Bayesian optimization, Hyperparameter optimization: online learning and gradient-based methods, Concentration inequalities: basics, Concentration inequalities: sub-Gaussian and sub-Exponential random variables, Random Fourier Features, Randomized Linear Algebra.