CSW4701 
ARTIFICIAL INTELLIGENCE

 

Spring 2011

Class Pictures: In class and CVN

Tuesday/Thursday 2:40-3:55PM

535 Mudd
CVN Course

 

Salvatore J. Stolfo
606 CEPSR
212.939.7080

Email: sal@cs.columbia.edu

Office Hours: 1 hour prior to class

URL of this page:

http://www.cs.columbia.edu/~sal/AI-Spring11.htm

FINAL EXAM SCHEDULE
(Definitely the last class)

 

Description: Description: Description: Description: C:\Documents and Settings\Sal\My Documents\AI.small.gif

Can you guess what this picture means?

Class picture

And another class picture

 

To access the course discussion board
1. Go to http://courseworks.columbia.edu and log in
2. Click the link for COMS W4701 course listing
3. Click the link for "Discussion" on the left-hand side

 

 

TEXT

 

A special subset of :

Artificial Intelligence, A Modern Approach,
Russell and Norvig, (Prentice Hall), THIRD EDITION,
ISBN:
0558881173

URL: http://aima.cs.berkeley.edu/

Syllabus

 

- Overview of AI: Strong, Weak, History, Symbolic AI/Cognitive AI

- Introduction to LISP: Examples (LISP is NOT required for projects)

- Assignment #1: A set of questions from the R&N text book

- Problem Solving:

* Problem Formulation as Search, State Spaces, Problem Reduction

* Basic Weak Search Methods & Algorithms: Breadth, Depth, Best-first, Generate and Test, Hill Climbing, etc.

* Assignment #2: Implementation of a basic search method for a moderate-scale problem, comparative evaluation of alternative search algorithms.

* Game Playing: Minimax, Alpha-Beta

*Assignment #3: Game Playing Tournament, TBA

- First Order Logic (Deduction)

* Mechanical Theorem Proving

* Unification

* Gödel’s theorems, SAT and relationship to NP-Completeness

- Midterm: Closed Book

- Knowledge Representation:

* Structured Representations: Semantic Nets, Frames, Blackboards, Rules

- Uncertainty and Bayesian Inference (Abduction)

- Machine Learning and Generalization (Induction)

* Inductive Inference

* Version Spaces, ID3, CART, etc.

* Bayesian Learning

* Support Vector Machines

* Assignment #4: Implementation of a machine learning program classifier

- Final Exam: Closed Book, Entire Material Presented in Class

 

There are many code examples on the AIMA website to guide your work in LISP and Java.

 

LISP IS NOT REQUIRED FOR THE PROJECTS.

 

[For those who are brave enough:

LISPworks (http://www.lispworks.com/) is free and probably the easiest LISP implementation for you to use. The course structure by lecture is specified in the table below, annotated with required book chapters from Russel & Norvig’s AIMA text. Useful slides/code/background material are provided in the right most column. Some of these are likely to change from time to time.]

The basic required chapters of AIMA are 1-4, 5-10, 13 and 18, or 1-14 in the Custom version of the text.

We will follow a general theme throughout the progression of the course describing alternative styles of logical inference, from Deductive Inference, to Abductive and finally Inductive Inference in the context of an intelligent agent architecture. Auto-epistemic will have to wait for another course.

 

DETAILED COURSE SCHEDULE

Session

Date

Topic/chapter

Free code/

HW Project Assigned or Due

1

1/18

Overview of AI (Chapter 1 and 2)

Intro-Slides and Agents, Symbolic AI/Cognitive AI

2

1/20

History of AI/LISP

Project#1: Updated Description of: Simple Pattern Matcher

3

1/25

Intro to LISP

(to understand code examples)

 

Equality of  symbol structures

How to run Lisp

Download personal edition Lisp from www.lispworks.com
LISP Primer
Load and compile in Lispworks

See http://www.cs.berkeley.edu/~russell/code/doc/install.html

4

1/27

Intro Problem Solving (Chp 3)
State Space Search

Search slides

5

2/1

Weak search methods&algs
(BFS, DFS, etc.) (Chp 3)

Basic search,
Iterative DFS search,
8 Puzzle state

Convert states to symbols

6

2/3

IDDFS, Complexity measures

Project #1 DUE

7

2/8

Uniform cost, Greedy,
Bidirectional Search

 

8

2/10

Heuristic Search A* (Chp 4)

Project#2: Search programs,
An improved NORTH operator for 15 puzzle
Uniform cost, Greedy search, Bidirectional search

9

2/15

Problem-reduction problem solving, Constraint satisfaction problems (Chpt 6)

A star search,
Iterative A star search

CSP slides

10

2/17

AND/OR

A*-And-Or-Search

11

2/22

Game Playing

(Chp 5)

Project#3: Isolation

Game Playing Slides

 

12

2/24

Minimax/Alpha-beta

Project #2 DUE THURSDAY FEB 24

 

Minimax/Alpha-Beta

13 

3/1

Beyond Classical Search (Chpt 4)

 

Heuristic Admissibility/consistency

A*and local search heuristics

Local search

14

3/3

Hill Climbing/Simulated Annealing/Genetic Algs

More local search

15

3/8

MIDTERM

All material on search, up to the lecture on 3/3

1 hr 15 min. time limit

Propositional logic is NOT covered on the exam.

16

3/10

Intro to Knowledge Representation

 

Propositional Logic

Mechanical Theorem Proving (Chpt 7,8)

Propositional Logic Slides

Propositional Logic Slides 2

 

3/14-3/18

SPRING BREAK

 

17

3/22

Resolution Thm. Proving (Chp 9)

 

 

Inference Slides

 

18

3/24

First Order Logic, Godel Thms. (Chp 8)

Resolution Thm Proving in FOL

(Chp 9)
Unification, Herbrand Universe

FOL slides

 

19

3/29

More logic

Theorem Proving Code & examples

 

Project #3 DUE TUESDAY 29 MARCH FOR ALL INCLASS AND CVN STUDENTS .

 

20

3/31

Semantic nets/Frames
Inference in nets (Chp 10)

TOURNAMENT PLAYOFFS FRIDAY APR 1

About the winning program

Pictures from the Tournament Play

Sustaining energy levels.

The Intense Championship Round

Winner Board Photo

 

21

4/5

Frames, Rule-based Systems
Backward chaining, Prolog

Intersection search 

22

4/7

Uncertainty (Chp 11, 12)

Uncertainty slides

 

23

4/12

Bayesian Inference

Bayesian inference slides

24

4/14

Intro to Machine Learning (Chp 13)

Learning Slides

Project #4: Decision Tree Learning

25

4/19

Generalization, Inductive Inference (Chp 14)

Chpt19-slides

26

4/21

Decision Tree Learning

ID3 and example

27

4/26

Naive Bayes Classifier

NB-slides

28

4/28

LAST CLASS – INCLASS FINAL EXAM

 

A EYE

Resurgence of AI – or more of the same?

http://www.nytimes.com/2009/12/08/science/08sail.html

**

5/3 –
5/6

STUDY DAYS

Project #4 DUE MON 2 MAY, 2011

 

**

5/5 – 5/8

FINALS WEEKS

End of Spring 2011 TERM. Summer Break begins.

 


GRADING POLICY

 

Click Here

 

Project Submission Instructions

 

Click Here

 

 

How to use CLIC lab machines

 

AIMA Code base:

Just visit their link http://www.cs.berkeley.edu/~russell/code/doc/install.html

for details.

TA DETAILS

 

Name: Tianju Wang (Head TA)

E-mail:  [tw2326@columbia.edu]

Office:  TA Room

TA office hours: Wed 10:00AM-12:00PM

 

Name: Kwan-I Lee

E-mail:  [kl2549@columbia.edu]

Office:  TA room

TA hours:  TUESDAY 4:10-6:10PM

Name: Aditya Bir

E-mail:  aab2178@columbia.edu

Office:  TA Room

TA office hours:  Friday: 9:00 to 11:00

 

FINAL GRADE DISTRIBUTION

 

Final grades are curved. The distribution is tentatively set at

 

HW/Test

Percentage

Project #1

15%

Project #2

15%

Project #3

20%

Project #4 

15%

MIDTERM

15%

FINAL

20%