CS 446 - Machine Learning
|Machine Learning||ONL||62698||ONL||-||Julia Constanze Hockenmaier|
This course studies 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 are covered. This course also 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 or 4 hours
A course in theory of computation (such as CS 373) and an artificial intelligence course (such as CS 440).