MET CS 767
Theories and methods for automating and representing knowledge with an emphasis on learning from input/output data. The course covers a wide variety of approaches, including Supervised Learning, Neural Nets and Deep Learning, Reinforcement Learning, Expert Systems, Bayesian Learning, Fuzzy Rules, Genetic Algorithms, and Swarm Intelligence. Each student focuses on two of these approaches and creates a term project. Laboratory course. Prereq: MET CS 566; or instructor's consent. It is also recommended that students enroll in this class only after taking the core courses for MS in Computer Science.
FALL 2015 Schedule
|D1||Braude||MCS B33||R 6:00 pm-9:00 pm|