# 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.
### Grader

The grader for this course will be Azam Asl aa2821@nyu.edu.
### Problem Sets and Programming Assignments

Problem set 1. Due Feb. 8

Solution set 1.

Programming Assignment 1 Due Feb. 15

Problem set 2. Due Feb. 29

Programming Assignment 2 Due Mar. 7

Problem set 3. Due Mar. 21

Solution set 3.

Programming Assignment 2 Due Apr. 11

Problem set 4. Due Apr. 11

Solution set 4.

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.
Outline of Final Exam

Sample Final Exam

Sample Final Exam Solutions