Machine Learning

MET CS 767

Theories and methods for automating the solution of problems with inexact specifications, input, models, or output (e.g., text checkers, help desks). Expert systems, fuzzy methods, neural net architectures, and genetic algorithms are examined and compared. Algorithms and a term project are implemented using shells and C++ or Java. 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

Section Instructor Location Schedule Notes
D1 Braude MCS B33 R 6:00 pm-9:00 pm

Note that this information may change at any time. Please visit the Student Link for the most up-to-date course information.