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**:

- Midterm Exam: about 20%
- Final Exam: about 20%
- Homework Assignments: about 60%

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%