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.
Exclusion
Distribution Requirements
Science
Breadth Requirements
The Physical and Mathematical Universes (5)
Mode of Delivery
In Class