Unsupervised and Reinforcement Learning
|Lecture hours per week||2|
|Lab hours per week||2|
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.