Unsupervised and Reinforcement Learning

In the first half of this course, students will be exposed to unsupervised learning concepts, examples and algorithms, such as data clustering concepts, k-means clustering, the expectation-maximization algorithm and the Gaussian mixture models, principal components analysis, independent component analysis and factor analysis.

In the second half of the course, students will be exposed to reinforcement learning concepts and algorithms.  

Note: Check with the institution regarding start/end dates, prices, and delivery method. These may vary according to program, section, and/or semester.