DATA-55100 Unsupervised Machine Learning

This course will survey leading algorithms for unsupervised learning and high dimensional data analysis. The first part of the course will cover clustering algorithms and generative models of high dimensional data, such as distance/similarity measures, k-means clustering, hierarchical clustering, Fuzzy C-Means (FCM), Possibilistic C-Means (PCM), Principal Components Analysis (PCA), and Linear Discriminant Analysis (LDA). The second part of the course will cover spectral methods for dimensionality reduction, including multidimensional scaling, spectral clustering, and manifold learning. The third part of the course will cover self-organizing maps (SOMs) as well as an introduction to semi-supervised learning. Other, recent developments and state-of-the art methods related to unsupervised learning may also be covered. 

Credits

3

Prerequisite

CPSC 50200 or DATA 50000, and CPSC 50100, DATA 51100, or prior programming experience