CSC412H1: Probabilistic Learning and Reasoning

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.

CSC311H1/ STA314H1/ CSCC11H3/ CSC311H5 Prerequisite for Faculty of Applied Science and Engineering students: ECE421H1/ ROB313H1
STA414H1, STAD68H3. NOTE: Students not enrolled in the Computer Science Major or Specialist program at A&S, UTM, or UTSC, or the Data Science Specialist at A&S, are limited to a maximum of 1.5 credits in 300-/400-level CSC/ECE courses.
The Physical and Mathematical Universes (5)