Probabilistic Learning: Theory and Algorithms

The course will provide a tutorial introduction to the basic principles of probabilistic modeling and then demonstrate the application of these principles to the analysis, development, and practical use of machine learning algorithms. Topics covered will include probabilistic modeling, defining likelihoods, parameter estimation using likelihood and Bayesian techniques, probabilistic approaches to classification, clustering, regression, and related topics such as model selection, bias/variance, and density estimation.
Course material created by Professor Padhraic Smyth.

Target audience: Graduate

Institution: University of California, Irvine

Materials available: Problem sets or projects, Textbook recommendations

Products: MATLAB