CS567-Machine Learning USC

This repository is my homework solution for CSCI567-Maching Learning for fall 2019 at USC.

It covers some of the main models and algorithms for regression, classification, clustering and Markov decision processes. Topics includes linear and logistic regression, regularization, probabilistic (Bayesian) inference, SVMs and kernel methods, ANNs, clustering, and dimensionality reduction. The course uses the Python programming language and assumes in addition familiarity with linear algebra, probability theory, and multivariate calculus. This course is designed to give graduate-level students a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who apply machine learning to a whole host of applications.