CS471 Fall 2008 Time: MWF 1:30-2:20pm Location: LWSN B134
Professor Jennifer Neville
Lawson 2142D neville[at]cs.purdue.edu 6-9387
Office hours: M 2:30-3:30pm
Lawson B116 pangin[at]cs.purdue.edu X-XXXX
Office hours: MWF 3:30-4:30pm
This course provides an introduction to foundational areas of artificial intelligence and current techniques for building intelligent systems. Topics will include: problem solving, state-space representation, heuristic search techniques, game playing, knowledge representation, logical reasoning, reasoning under uncertainty, machine learning, and natural language processing.
CS251: Data Structures.
S. Russell and P. Norvig (2002). Artificial Intelligence: A Modern Approach. Prentice Hall, 2nd edition.
Homework assignments will be posted to Blackboard, and they should be submitted there, unless otherwise noted. In general, questions about the details of homework assignments should be directed to the TA, though you should feel free to mail the instructor whenever you have a question. Solutions and grading criteria will be available on Blackboard when homework is returned to students.
Exams will be closed book and closed notes. Review sessions will be held in the class before each exam. The exam questions will focus on conceptual understanding of the key ideas from the course, including problem definitions, algorithms, and data structures.
Introduction (1 week)
What is artificial intelligence? Overview of AI history and associated application areas.
Search (3 weeks) Problem solving as search, heuristic search, constraint satisfaction, adversarial search.
Reasoning with uncertain knowledge (3 weeks)
Basic probability and statistical reasoning, Bayesian and Markov networks, exact and approximate inference methods.
Learning (3 weeks)
Learning as search, parameter estimation, structure learning, overfitting, bias/variance.
Reasoning with logic (2 weeks)
Propositional logic and first-order logic, logical reasoning and inference.
Reasoning with logic and uncertainty (1 week)
Combining Bayesian and Markov networks with first-order logic, including representation, learning and inference algorithms.
Natural language processing (1 week)
Applications of machine learning and probabilistic inference to natural language processing tasks.