Graduate

Courses

Computer Science

B553 Neural and Genetic Approaches to Artificial Intelligence

Credits: 3

Prerequisite(s): CSCI-B 551.

Approaches to the design of intelligent systems inspired by nervous systems, evolution, and animal behavior. Distributed and perceptually grounded representations. Temporal processing. Perception and action. Genetic search. Unsupervised and reinforcement learning. Comparison of symbolic, subsymbolic, and hybrid approaches to intelligence.

  • Course History

      Spring 2013


      Instructor: David Crandall
      Time: 11:15AM-12:30PM Tue, Thu
      Location: ED1225
      Course File (syllabus or course advertisement)
      Supplementary Description: CS B553: Probabilistic approaches to Artificial Intelligence (This course is officially known as Neural and Genetic Approaches to Artificial Intelligence, but that is not an accurate description of the course content this semester, Spring 2013.)

      Spring 2012


      Instructor: Kris Hauser
      Time: 11:15AM-12:30PM Tue, Thu
      Location: Informatics East, Room 122
      Course URL (syllabus link or course homepage)
      Course File (syllabus or course advertisement)
      Supplementary Description: This course will debut a new B553 syllabus intended as a standardized 2nd year graduate AI course: Algorithms for Optimization and Learning. Prerequisites: B551 or equivalent, calculus, linear algebra Topics: Application and implementation of optimality principles to design, decision making, and inference. Learning and probabilistic inference in graphical models, expectation maximization, and temporal sequence processing.