151A: CSE 151A: Introduction to Machine Learning

This is a broad introduction to machine learning. Covered topics include classification, regression, and conditional probability estimation; generative versus discriminative approaches to classification; nearest neighbor methods; Gaussian generative models; linear models, such as logistic regression and support vector machines; kernel machines; decision trees and ensemble methods including boosting and random forests; neural networks; generalization theory; and an assortment of frontier topics (e.g., semisupervised and active learning) as time permits.