# Artificial Intelligence

G22.2560
Wednesday, 5:00-7:00.
Warren Weaver Hall room 102.
Professor Ernest Davis

### Reaching Me

• Email:
• phone: (212) 998-3123
• office: 329 Warren Weaver Hall
Office hours: Tuesday 10:00-12:00, Thursday 3:00-4:00

### Textbook:

Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig (3rd edition)

### Prerequisites:

Fundamental algorithms.

### Requirements:

Problem sets (collectively 30%), small programming assignments (20%), final exam (50%).

### Description:

There are many cognitive tasks that people can do easily and almost unconsciously but that have proven extremely difficult to program on a computer. Artificial intelligence is the problem of developing computer systems that can carry out these tasks. This course will cover problem solving, automated reasoning, and machine learning.

### Course topics:

• Introduction
• Search
• Games playing
• Logic and inference
• Reasoning with Uncertainty
• Machine Learning

### Instructions for class email list

Link to the class email web page and follow the instructions there for subscribing.

The grader for this course will be Azam Asl aa2821@nyu.edu.

### Problem Sets and Programming Assignments

Problem set 1. Due Feb. 8
Programming Assignment 1 Due Feb. 15
Problem set 2. Due Feb. 29
Programming Assignment 2 Due Mar. 7
Problem set 3. Due Mar. 21
Programming Assignment 2 Due Apr. 11
Problem set 4. Due Apr. 11
Programming Assignment 4 Due May 2

### Class notes

Article by Garry Kasparov about Computer Chess. (New York Review of Books, 2/11/2010)
Propositional Logic
Davis-Putnam algorithm
Davis-Putnam: example.
Predicate calculus
Guide to expressing facts in first-order logic
Notes on probability and random variables are on the course Blackboard site.
ID3 Algorithm
ID3 Example
Independent Evidence
Naive Bayes for Text
Locality Sensitive Hashing
80 Million Tiny Images: A Large Dataset for Non-parametric Object and Scene Recognition A. Torralba, R. Fergus, and W. Freeman
Linear Separators and Support Vector Machines
Entropy
Clustering Algorithms
Minimum Description Length Learning

### Late policy on assignments

Assignments are due at the beginning of class on the due date. I will accept it up to one week late with a penalty of 1 point out of 10. It may be submitted either in hard-copy (preferred) or by email to the TA in plain-text, PDF, or Word.

### Cheating

You may discuss any of the assignments with your classmates (or anyone else) but all work for all assignments must be entirely your own. Any sharing or copying of assignments will be considered cheating. By the rules of the Graduate School of Arts and Science, I am required to report any incidents of cheating to the department. My policy is that the first incident of cheating will result in the student getting a grade of F for the course. The second incident, by GSAS rules, will result in expulsion from the University.

### Final Exam

The final exam will be given on Wednesday May 9, 5:00-7:00, WWH 202.