Introduction to Machine Learning

Catalog description

Survey of techniques for development and analysis of software that learns from experience. Specific topics: supervised learning (classification, regression); unsupervised learning (density estimation, clustering, dimensionality reduction); reinforcement learning; and others. Specific techniques: linear classifiers, mixture models, nonparametric methods, decision trees, neural networks, kernel machines, ensembles, graphical models, Bayesian methods, etc.