Subject:
Computer Science (CS)
Catalog number:
786
Unit weight:
0.50
Meet type:
LEC
Grading basis:
NUM
Cross-listing(s):
N/A
Requisites:
Antireq: CS 786P
Description:
Covers the fundamental principles of probabilistic inference and computational learning systems. Topics include Bayes decision and utility theory, Monte Carlo and Markov chain Monte Carlo methods; learning with complete data; Bayesian networks, Markov random fields and factor graphs; models; learning with incomplete data; computational learning and PAC learning theory.
Topic titles:
N/A
Faculty:
Mathematics (MAT)
Academic level:
GRD
Course ID:
000726
