Computer Science 247 - Machine Learning

Machine Learning algorithms allow computers to be taught to perform tasks without being explicitly programmed. This course is an introduction to machine learning and data mining. The course will explore supervised, unsupervised, ensemble and reinforcement learning. Topics may include: decision tree learning, rule learning, neural networks, support vector machines, Bayesian learning, clustering, hidden Markov model learning, and/or deep learning. The material of this course has some overlap with that of Computer Science 241, but it is permissible to take both.

In the fall, this course will be fully accessible online and may also make use of in-person meetings.

Requisite: COSC 211. Fall semester.  Visiting Professor Spector.