CSE 5103
Theory of Artificial Intelligence and Machine Learning
Mathematical foundations for Artificial Intelligence and Machine Learning. An introduction to the PAC-Semantics ("Probably Approximately Correct") as a common semantics for knowledge obtained from learning and declarative sources, and the computational problems underlying the acquisition and processing of such knowledge. We emphasize the design and analysis of efficient algorithms for these problems, and examine for which representations these problems are known or believed to be tractable. Prerequisites: CSE 347
Instructors
Reviews
I learned so much from Brendan from every lecture! He actually cares about students absorbing the fundamental ideas about AI. He is knowledgeable, kind and easy-going. I am really sad to see many students here didn't appreciate all these at all... alas, if you only want an easy course, maybe just do data mining...
10/30/2024
Juba is not a good fit for 347, a challenging, required course which many CS students come in ready to hate. The disgruntled genius MIT grad that all professors aspire to be, his standoffish nature can put people off. If you come in interested in the stuff he talks about (theoretical CS) you'll learn a ton, but otherwise the material can be a grind
2/28/2024