Transcript Syllabus

CSE 571 (11147)
Artificial Intelligence
(MW 3:15 – 4: 30 PM, ECA A219)
Instructor: Chitta Baral
Office hours: Tuesday 2 to 5 PM
Meaning of the word: ``intelligence''
• 1 (a) The capacity to acquire and apply knowledge.
(b) The faculty of thought and reason.
(c) Superior powers of mind. See Synonyms at mind.
• 2
An intelligent, incorporeal being, especially an angel.
• 3
Information; news. See Synonyms at news.
• 4 (a) Secret information, especially about an actual or potential
enemy.
(b) An agency, staff, or office employed in gathering such
information.
(c) Espionage agents, organizations, and activities considered as
a group
• Source: The American Heritage® Dictionary of the English
Language, Fourth Edition Copyright © 2000 by Houghton Mifflin
Company. Published by Houghton Mifflin Company. All rights
reserved.
Meaning of the word: ``intelligence''
• n
• 1:
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the ability to comprehend; to understand and profit from
experience [ant: stupidity]
2: a unit responsible for gathering and interpreting intelligence
3: secret information about an enemy (or potential enemy); "we
sent out planes to gather intelligence on their radar coverage"
4: new information about specific and timely events; "they awaited
news of the outcome" [syn: news, tidings, word]
5: the operation of gathering information about an enemy [syn:
intelligence activity, intelligence operation]
Source: WordNet ® 1.6, © 1997 Princeton University
Artificial Intelligence
• Based on the above, `artificial intelligence'
is about the science and engineering
necessary to create artifacts that can
– acquire knowledge, i.e., can learn and extract
knowledge; and
– reason with knowledge (leading to doing
tasks such as planning, explaining,
diagnosing, acting rationally, etc.),
Two main parts of this course
• Knowledge representation, reasoning (and
declarative problem solving)
– 60% from the text book -- Aug 25th to Oct 22
– 10% from the book `Causality' by Judea Pearl
and papers by Judea Pearl and Joe Halpern;
and on Bayes' nets -- Oct 29th, Nov 3, 5
• Learning
– 15% on learning logical rules such as Progol, FOIL
etc -- Nov 10,12,17;
– 15% on learning Bayes' nets, causal structures etc. -Nov 19-26, Dec 1
Syllabus from the text book
• Chapter 1 (Sections 1.1-1.3) .
• Chapter 2
• Chapter 3 (Sections 3.1, 3.1.1-3.1.3, 3.1.5, 3.2, 3.2.1,
3.2.4, 3.4, 3.4.1)
• Chapter 4
• Chapter 5 (Sections 5.1-5.4, 5.6)
• Chapter 8 (Sections 8.1-8.3)
• Time line:
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Aug 25, 27, Sept 3, 8 -- Ch1, Ch 8 (Smodels and DLV syntax)
Sept 10, 15, 17, 22
-- Ch 2 and 3
Sept 24, 29, Oct 1
-- Ch 4
Oct 6, 8, 13, 15, 20, 22 -- Ch 5, some of Ch 8
Several papers for the other parts (to be listed)
Grading
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Two tests (No finals) 30%
– Test dates (Test 1 -- Oct 27; Test 2 -- Dec 3)
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One project 30% (Due by Dec 5th, demo during Dec 1-5)
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Sept 29th -- I will suggest a list of ideas
Start discussing mine and other ideas by Oct 1st
Finalize project proposal by Oct 15th;
First Status report Nov 5th
Second Status report Nov 17th
Homework & programming assignments 20%
Class notes (making slides -- due in 7 days) 10%
Class participation 10%
– percentage of classes you have asked non-trivial question and answered
questions; attendance will be taken in every class;
– coming late after the attendance has been taken will result in being marked
absent and will count negatively.
• first class disruption -- arriving late or a similar activity - without prior permission will
count -1% of the grade; the next one -2%; and so on.)
Modus Operandi
• Students will be assigned material to read.
• They have to come prepared to the class where
I will ask questions and clarify things.
• This will happen during the first 60 minutes of
the class. In the last 15 minutes of the class I will
motivate the content to be discussed in the next
class.
• Each class one student will take notes and make
slides.