Artificial Intelligence (W4701)
General Information for Fall, 1999
V. Siegel (
email: evs at cs dot columbia dot edu
). Office: 703 CEPSR, 939-7112
Eric Siegel now provides predictive analytics services at Prediction Impact, Inc., where predictive modeling and data mining are applied to gain customer intelligence.
- Lalitha Agnihotri (email@example.com)
- Grace Junxin Zhang (firstname.lastname@example.org)
- Chris Vaill (email@example.com)
Despite its relative youth, Artificial Intelligence is a vast field,
since it is all-inclusive of systems designed to be intelligent or to
approach a task that is considered to require intelligence.
Therefore, the list of main topics below is only the very top of a
deep hierarchy. Although we can only cover the upper portions of this
hierarchy this semester, we will delve into greater detail in certain
- problem solving with search
- knowledge representation
- logical inference
- reasoning with uncertainty
- machine learning
Other topics incorporated, time permitting:
There will not be time to cover all applications of AI; most of
vision, speech understanding and robotics are omitted.
- Advanced techniques
- temporal reasoning
- constraint satisfaction problems
- hybrid AI systems
- Meta-level issues
- evaluating AI systems
- top 20 AI buzzwords
- philosophical issues
- cognitively plausible techniques
- AI applications
- expert systems
- natural language processing
- games and entertainment
- data mining
On-line homework projects
Additionally, each project will include extensive written exercises.
Knowledge of Java may be desirable for some of the project,
but in general we allow you to do the homework in the
language of your choice.
Note that this list is subject to change.
- Search Methods: Quest for Palindromes
- Knowledge representation: what and when
- Logic: rules, inference, planning and uncertainty
- Machine induction: concept learning and decision trees
W3139 (Data Structures and Algorithms). Please do not forget that
W3203 (Discrete Math) is a co-requisite for W3139 and is thus also a
prerequisite for this class.
Artificial Intelligence: A Modern Approach, by Russel and Norvig,
Prentice Hall, 1995. ISBN # 0-13-103805-2.
Readings will also included papers handed out
25% final exam.
To receive a passing grade, you must complete satisfactory work in
every area. In other words, you must receive passing grades for your
and a passing grade on your midterm and final.
Late assignments will not be accepted without the TA's prior permission.
Extensions are available only in the case of dire emergencies.
You should hand in what you have on time, since
partial credit will be considered for all incomplete work.
Each student is required to obtain an extended Columbia student
machine account. This account is included in the regular fees for
Columbia University undergraduates. For all other students (graduate
school, Barnard, Teacher's College, etc.), there is a
Course web page A web page will be set up and communication
with the class pertaining to assignments, grades, changes in
requirements etc. will be via this medium. The class web page is
http://www.cs.columbia.edu/~evs/ai/. This page will have further links
to pages with handouts, demos, etc. Students are required to log in
and check this web site at least every few days to make sure they are
up to date with any information pertaining to the course. The Web site
is the primary means of getting information to you
outside of class.
Collaboration: Discussion of material covered in class is strongly
Some assignments may be designated as
collaborative. Otherwise, the work you submit must be your own work.
In case of grading difficulties, the order of appeals will be the TA
who graded your submission, the head TA (if not the same TA), and then the
instructor. All rulings by the instructor are final.
Open Door Policy: We would like the course to run smoothly and
enjoyably. Feel free to let us know what you find good and interesting
about the course. Let us know sooner about the reverse. See us, leave us
a note, or send us an e-mail.
F99 Announcements (reverse chronological):
- I am putting the final exams (two cardboard holders) and remaining
homeworks (one holder) on the file cabinets at the cs deptartment offices.
- Homework 3 solutions
- Homework 4 solutions
- On 12/20/99, Chris said: I posted a message to the ng that I'll be giving back homeworks from
noon to 4 in my office. Unfortunately, I have a final at 4, so I
can't do much after that. Before I go to my final, I'm going to put
the hw3s in or on the file cabinet outside the cs dept office so
people can still get them.
- Hybrid Allen-STRIPS question I spoke
about during the final lecture.
- HW4 lateness policy: We have decided to lower the penalty to 4
points per half per day. (So, if you hand in one half on time, only
the other half is being penalized). But the final possible date will
be by the end of Dec 14.
- I have just added a third, required, testing example
to homework 4 -- you must show
your system working on this example.
- Relavent links for your interest:
- Trace of FIND-S applied to the
- Homework 4:
Horn Clause Learning System -- it's cool!
- Homework 3:
Forward-Chaining Logical Inference System -- have fun!
- Results of the palindrome project.
- HW1 Solutions Includes updated solution for 4.8!
- Lyrics to the
Knowledge Representation Rhapsody.
- Homework 2:
Knowledge Representation: What and When
- You have got to check out this animated palindrome GIF by classmate
How hard is chess? (by David Gelertner) and How Intelligent is Deep Blue? (by Drew McDermott).
- Would anyone like to integrate the
80,000 words at
http://personal.riverusers.com/~thegrendel/enable10.zip -- the Scrabble
tournament word list), to see if it adds more words
to our palindrome dict? If you do, email me and I'll
integrate it for the rest of the class!
Class newsgroup (columbia.fall.cs4701)
- Course syllabus
- Homework 1:
Search: Problems, Practicalities, and Palindromes (ps file)
- Palindrome code for Homework 1.
- Try to get
the U2 rock band over the bridge in time, as shown in class.
- Course notes that I made for some
of the lectures. These lecture notes are outlines
that guided the talk -- you will not get much more than
a general outline from
them unless you have seen the lecture. But they
can be helpful reviewing resources, e.g., for the midterm.
Also note that within each lecture, the order of
the slides is often different from how it was presented in class.
email: evs at cs dot columbia dot edu