CS540-2: Introduction to Artificial Intelligence
Spring 2014 

Lectures: MWF 11:00am - 11:50am, Computer Science 1221 Instructor: Bryan R. Gibson, Graduate Student in Computer Sciences Office: CS 3395 E-mail: bgibson@cs.wisc.edu Office Hours (you can come to any one of us): Bryan Gibson (instructor): Fridays 1-2pm or by appointment, CS 3395, bgibson@cs.wisc.edu Chuck Dyer (instructor for other section): Monday, Wednesday 2-3pm, CS 6379, dyer@cs.wisc.edu TA: Han Li, Friday 3-4, CS 1309, hanli@cs.wisc.edu TA: Mohammed Ansari, Thursday 12-1, MSC 6795, ansari@cs.wisc.edu TA: Lichao Yin, Wednesday 3-4, CS 5395, lichao@cs.wisc.edu Discussions via Piazza (need a wisc.edu email address to sign up here) Homeworks Exams Handin and Grades via Moodle Topics (Reading) Unit 0: Introduction (lecture notes, Russell and Norvig (RN) chs 1,2) AI: history and today Unit 1: Search Uninformed Search: Breadth-first search, uniform-cost search, depth-first search, iterative-deepening (slides, RN ch 3) Informed search: A* algorithm (slides,RN ch 3) More search: Hill-climbing, Simulated annealing, genetic algorithms (slides(pg.15),GA slides,RN 4.1) Game playing: Minimax, alpha-beta pruning (slides, RN 5.1-5.3) Constraint Satisfaction Problems (slides) Unit 2: Machine Learning (math review) Intro to ML and Clustering (slides, Zhu and Goldberg (ZG) ch 1) HAC (HAC applet by M. Matteucci) k-means (k-means applet by M. Matteucci Classification (continuing with ZG ch 1.3) k-Nearest-Neighbor (slides (pg 21), knn demo) Perceptrons & Neural Networks (slides, addl. slides, RN 18.6-18.7) Support Vector Machines (slides, addl slides, RN 18.9, short tutorial, long tutorial, demo applet) Decision trees (slides, addl slides, RN 18.1-18.3, Example) Probability and Statistics Basics (slides, addl slides, RN 14.1, 14.2, 14.4) Bayesian Networks (slides, addl slides, tutorial (pp 1-23), Bishop PRML 8.2 for D-separation) Speech Recognition, Markov Models and HMMs (slides, E. Fosler-Lussier Markov Models and Hidden Markov Models: A Brief Tutorial) Unit 3: Logic Propositional Logic (slides) First-Order Logic (slides) Prerequisite: CS 367 Textbook: Artificial Intelligence: A Modern Approach, 3rd edition (blue cover, not green). 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%