CSC535

Probabilistic graphical modeling and inference is a powerful modern approach to representing the combined statistics of data and models, reasoning about the world in the face of uncertainty, and learning about it from data. It cleanly separates the notions of representation, reasoning, and learning. It provides a principled framework for combining multiple source of information such as prior knowledge about the world with evidence about a particular case in observed data. This course will provide a solid introduction to the methodology and associated techniques, and show how they are applied in diverse domains ranging from computer vision to molecular biology to astronomy.

015356

MATH 223 and MATH 313 or equivalent math background. MATH 464 or alternative course that covers basic discrete and continuous probability. CSC 445 or equivalent preparation in algorithms, data structures, and programming.

Spring