CSC412H1
UTSGProbabilistic 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.
View full details on the UofT Academic CalendarPrereq: CSC311H1/ STA314H1/ CSCC11H3/ CSC311H5 Prerequisite for Faculty of Applied Science and Engineering students: ECE421H1/ ROB313H1Breadth: Physical & Mathematical UniversesExcl: 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.
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Course Info
DepartmentCSC
CampusUTSG (St. George)
Level400
Hours24L/12T
BreadthPhysical & Mathematical Universes
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