ORIE 6741

Bayesian Machine Learning

Course Description

Course information provided by the Courses of Study 2016-2017.

This course aims to provide students with a strong grasp of the fundamental principles underlying Bayesian model construction and inference. We will go into particular depth on Gaussian process and deep learning models.  The course will be comprised of three units. 1. Model Construction and Inference: Parametric models, support, inductive biases, gradient descent, sum and product rules, graphical models, exact inference, approximate inference (Laplace approximation, variational methods, MCMC), model selection and hypothesis testing, Occam's razor, non-parametric models. 2. Gaussian Processes: From finite basis expansions to infinite bases, kernels, function spacemodelling, marginal likelihood, non-Gaussian likelihoods, Bayesian optimization.  3. Bayesian Deep Learning: Feed-forward, convolutional, recurrent, and LSTM networks.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: exposure to stochastic and statistical modeling and algorithms, or permission of instructor. 

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