Probabilistic Inference and Machine Learning

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