CS 730/830: Introduction to Artificial Intelligence

(Coordinator: Wheeler Ruml)

Catalog description

In-depth introduction to artificial intelligence, concentrating on aspects of intelligent problem-solving. Topics include situated agents, advanced search techniques, knowledge representation, logical reasoning techniques, reasoning under uncertainty, advanced planning and control, and learning. Prereq: CS 671.

Attributes

  • This course is one of the CS electives designated as implementation intensive.
  • This course can be combined with a CS–696W module to satisfy a Writing Intensive requirement.
  • This course can be taken as CS730H by honors students.

Outcomes

  • methodologies of software development: students can write working AI programs to solve states problems
  • principles of data structures: students can write efficient implementations of AI algorithms
  • fundamental computer science algorithms: students understand some of the fundamental algorithms developed in AI
  • principles and techniques of calculus, probability and statistics, and mathematical proof techniques: students understand and can use basic Bayesian statistics
  • think abstractly and reason logically about computer science problems: student understand several of the fundamental formalisms of AI
  • principles and techniques of a range of advanced topics in computer science: artificial intelligence and machine learning
  • good written and oral communications skills: student can write and present a comprehensive report on a course project
  • good understanding of general sciences and the scientific process: students know how to perform and write about a simple empirical investigation of an algorithm

Evaluation

Five assignments (60%), project (20%), three exams (20%).

Topics

  • Combinatorial Search:
    • heuristic search
    • constraint satisfaction
    • games
  • Certain Knowledge:
    • propositional logic
    • first-order logic
    • resolution refutation theorem proving
  • Planning:
    • STRIPS
    • progression, regression, and partial-order planning
    • planning graphs
    • Markov decision processes
  • Learning:
    • supervised learning
    • unsupervised learning
    • reinforcement learning
  • Uncertain Knowledge:
    • hidden Markov models
    • particle filters
    • Bayesian networks

Textbooks

Required:

  • S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, third edition, Prentice Hall, 2010.