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%