CSC412H1: Probabilistic Learning and Reasoning

Hours: 
24L/12T

An introduction to probability as a means of representing and reasoning with uncertain knowledge. Qualitative and quantitative specification of probability distributions using probabilistic graphical models. Algorithms for inference and probabilistic reasoning with graphical models. Statistical approaches and algorithms for learning probability models from empirical data. Applications of these models in artificial intelligence and machine learning.

Prerequisite: 
Distribution Requirements: 
Science
Breadth Requirements: 
The Physical and Mathematical Universes (5)