CS 446
Machine Learning

Credit:  3 or 4 hours.


Theory and basic techniques in machine learning. Presents the main theoretical paradigms and key ideas developed in machine learning in the context of applications such as natural language and text processing, computer vision, data mining, adaptive computer systems and others. Reviews several supervised and unsupervised learning approaches: methods for learning linear representations; on-line learning, Bayesian methods; decision-trees; features and kernels; clustering and dimensionality reduction. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 273 and CS 440.