Code & Notes from CS 188 Intro to Machine Learning with Prof. Sriram Sankararaman. They cover the introductory and essential topics of machine learning, starting with a review of probability and statistics, and then going into the rigorous mathematics behind several supervised learning algorithms such as logistic regression, SVMs, and decision trees, and then going into unsupervised techniques such as PCA, clustering, and gaussian mixture models. We end with random markov processes and hidden markov models. Problem sets and projects from the class are included as well.