Large-Scale Machine Learning

Introduction, Randomized Algorithms, Matrix Approximations (low-rank approximation, decomposition, sparse matrices, matrix completion), Large Scale Optimization, Kernel Methods (fast training), Boosted Decision trees, Dimensionality Reduction (linear and nonlinear methods), Distributed Gibbs Sampling, Sparse Methods/Streaming (sparse coding...); Applications.