CS 446

Credit: 3 OR 4 hours.

Theory and basic techniques in machine learning. Major theoretical paradigms and key concepts 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. Review of 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 373 and CS 440.