CSC412H1: Probabilistic Learning and Reasoning



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.

CSC311H1/ CSC411H1/ STA314H1/ ECE421H1/ ROB313H1/ CSCC11H3/CSC311H5
STA414H1. NOTE: Students not enrolled in the Computer Science Major or Specialist program at the FAS, UTM, or UTSC, or the Data Science Specialist at FAS, are limited to a maximum of three 300-/400-level CSC/ECE half-courses.
Distribution Requirements
Breadth Requirements
The Physical and Mathematical Universes (5)