CS 540 Section 1: Introduction to Artificial Intelligence
Fall 2012 

Lectures: MWF 9:55am - 10:45am, Noland 168 Instructor: Professor Jerry Zhu, jerryzhu@cs.wisc.edu, 608-890-0129 Office Hours (you can come to any one of us) Jerry Zhu: Thursdays 3-4pm 6391 Computer Sciences Bryan Gibson (instructor for the other section): Tuesdays 2:30-3:30pm 3387, Computer Sciences, bgibson@wisc.edu, 608-218-4250 Brian Nixon (TA): Mondays 12-1pm 1308 CS, nixon@cs.wisc.edu, (231)233-0955 Ara Vartanian (TA): Tuesdays 10-11 am 1301 CS, aravart@cs.wisc.edu, 818-355-3664 Piazza Homeworks Examinations Topics (reading list) Unit 0: Introduction (slides, textbook ch 1, 2) Unit 1: Machine Learning Clustering (slides, Z&G ch 1) Classification (optional reading: math crib sheet) k-Nearest-Neighbor classifier (knn demo) Decision trees (slides, 18.1-18.3) Support Vector Machines (slides, 18.9, short tutorial, long tutorial) Neural networks (slides, 18.7) Probability and statistics basics (slides, 14.1, 14.2, 14.4) Bayesian networks (slides, tutorial (pp 1-23), Bishop PRML 8.2 for D-separation) Unit 2: Search Uninformed search: Breadth-first search, uniform-cost search, depth-first search, iterative-deepening (slides, ch 3) Informed search: A* algorithm (slides, ch 3) More search: Hill-climbing, Simulated annealing, genetic algorithms (slides, 4.1) Game playing: Minimax, alpha-beta pruning (slides, 5.1 - 5.3) Game theory (slides, 17.5 - 17.6) Unit 3: Logic Propositional logic (slides, 7.1, 7.3 - 7.5) Unit 4: AI in the wild AI in speech recognition (slides, 15.1-15.3, 23.5, HMM tutorial) AI in natural language processing (guest lecture Prof. Ben Snyder from Computer Science) AI in computer vision (guest lecture Prof. Li Zhang from Computer Science) AI in robotics (guest lecture Prof. Bilge Mutlu from Computer Science) The I in AI (guest lecture Prof. Tim Rogers from Psychology) Prerequisite: CS 367 Textbook: Artificial Intelligence: A Modern Approach, 3rd edition (blue) Stuart J. Russell and Peter Norvig. Prentice Hall, Englewook Cliffs, N.J., 2010 Grading:Note: The distribution of CS540 final grades has been as follows. This is an approximation, and changes from semester to semester. The median student's course grade is usually a low B or high BC. The percentiles refer to ranking based on the final weighted score. A top ~25% of class AB next ~15% B next ~25% BC next ~20% C next ~10% D next ~3% F next ~2%