Introduction to all major topics in artificial intelligence including search, logic, optimization, constraint satisfaction, planning, multiagent systems, machine learning. The class prepares students for advanced study and research in each of the individual topics and provides students with tools to apply the approaches to numerous industrial applications. The assignments involve a mix of theoretical and implementation exercises.
Professor: Sam Ganzfried
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig (main textbook)
Operations Research Models and Methods by Paul Jensen and Jonathan Bard
Learn Python the Hard Way by Zed Shaw
Background: Students should be familiar with all the material in this document on mathematical proofs and with at least one standard programming language (e.g., Python, C, Java).
Project: For the class project students will implement an agent for 3-player Kuhn poker. This is a simple, yet interesting and nontrivial, variant of poker that has appeared in the AAAI Annual Computer Poker Competition. The grade will be partially based on performance against the other agents in a class-wide competition, as well as final reports and presentations describing the approaches used. Students can work alone or in groups of 2.
Evaluation: homeworks (every two weeks), midterm exam, final exam, class project
-- uninformed search, informed search, local search, adversarial search, constraint satisfaction
-- propositional logic, first-order logic, logical inference
-- integer optimization, linear optimization, nonlinear optimization
-- classical planning, spatial planning
-- Bayesian networks, hidden Markov models
6) Decision making
-- Markov decision processes, multiagent systems, reinforcement learning
7) Machine learning
-- classification, regression, clustering, deep learning
Lecture 1 (8/22)
-- assignment: read proof review document and Ch. 3.1-3.4 from Russell/Norvig textbook
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