CS 540 Section 1: Introduction to Artificial Intelligence
Fall 2016 

Lectures: T,Th 9:30am - 10:45am, Psychology 121 CS 1240 Instructor: Professor Jerry Zhu Office Hours (you can come to any one of us) Jerry Zhu: Th 2:30-3:30pm, CS6391, 608-890-0129, jerryzhu@cs.wisc.edu Tuan Dinh (TA): F 3pm, CS5384. tuandinh@cs.wisc.edu Li Liu (TA): W 4pm, CS7370. lliu262@wisc.edu Aparna Subramanian (TA): M 4pm, CS6397, aparnasubr@cs.wisc.edu Homeworks Examinations Tentative Topics (reading list) Introduction (AAAI, Stanford One Hundred Year Study on Artificial Intelligence, textbook ch 1, 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) Detour: Probability and statistics basics (slides, 14.1, 14.2, 14.4) 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) Data Analytics and Machine Learning Clustering (slides, Z&G ch 1 [free access from UW IP addresses]) Classification (optional reading: math crib sheet) k-Nearest-Neighbor classifier (knn demo) Decision trees (slides, 18.1-18.3) Linear regression (notes) Neural networks (slides, 18.7, hand notes on backprop) Naive Bayes classifier (slides, tutorial (pp 1-23), Bishop PRML 8.2 for D-separation) Logic Propositional Logic (slides, 7.1, 7.3 - 7.5) First Order Logic (slides, FOL inference slides) 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%