Advanced Intelligent Systems (W4721)
This is an advanced course in artificial intelligence (AI), covering a
wide range of AI methods, concepts and applications. It is
effectively "AI part II", a sequel to the AI intro course (W4701).
The first portion of the course will be oriented around
well-structured homework assignments, and in the later portion you will focus
on implementing a larger, open-ended project.
Note that this generalizes from the bulletin's and CVN's course
description to expand the focus beyond expert
systems per se (although the concepts of expert systems will be
covered, and an expert system is an option for your
Professor: Eric V. Siegel (
email: evs at cs dot columbia dot edu
Office hours: Tues 4:30 - 5:30, Wed 2:00 - 3:00.
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.
TA: Jeff Commando Sherwin (email@example.com).
Office hours: TBA
Framework of AI Approaches for this course:
There will not be time to cover all applications of AI; most
vision, speech understanding and robotics are omitted.
The order of lecture topics will be altered
from the above framework in order to support
the following sequence of homework projects.
- Symbolic AI and knowledge representation, such as:
- terminological knowledge
- concept subsumption inference
- ontological engineering, e.g., temporal representation and reasoning
- Probabilistic AI, such as belief networks
- Machine learning, such as:
- neural networks
- induction over belief networks
- computational learning theory
- unsupervised learning
- Applications, such as:
- computational linguistics
- data mining
- expert systems, such as medical diagnosis
- Advanced and misc. topics, such as:
- hybrid systems
- artificial life
- AI buzzwords galore
Also, written work on probability will be included in HW2 and on the midterm.
- Temporal constraint inference; application thereof
- Neural networks for image classification, e.g., face recognition
- Short oral presentation of a published research paper
- Final project (optionally can build on earlier homework)
The last two projects above facilitate learning
about AI research, including:
- Understanding research papers
- Performing experimental or system-based research
- Writing a report of research results
- Orally presenting research results
A course in artificial intelligence; or permission of the instructor
- The semantics of linguistics and temporal reasoning
- Representation issues: modularity, "Chomsky status"
- Overfitting versus generalization
- Evaluating results
Text: Readings will included research papers handed out
in class, and a few later chapters from
Artificial Intelligence: A Modern Approach, by Russel and Norvig,
Prentice Hall, 1995. ISBN # 0-13-103805-2.
8% paper presentation,
31% final project,
17% final exam.
Late assignments will be penalized.
Note that partial credit will be considered for all incomplete work.
Collaboration: Discussion of material covered in class is strongly
Some assignments will be designated as
collaborative. Otherwise, the work you submit must be your own work.
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.
email: evs at cs dot columbia dot edu
INFORMATION FROM PREVIOUS OFFERING OF THIS COURSE:
STUDENT FINAL PROJECTS:
PRESENTATION SCHEDULE -- ignore the one
off jeff's page.
- Please copy your writeups, etc. into Eric's AcIS directory ASAP. Thanks.
- Eric needs hardcopies of final project writeups ASAP under his office door.
- Paper presentation grades are available off Jeff's course page
(link below). Grades are out of 10 possible points (despite the fact
that it is 8% of the semester grade). Grading was based on
organization, prepared slides, demonstrated understanding of paper,
conveyed basic content, and conclusions/personal response.
- Final exam -- due Monday, May 10.
- FINAL PROJECT WRITEUP ASSIGNMENT
- FINAL PRESENTATION ASSIGNMENT
- Guidelines for the final presentation are coming VERY soon.
- After you have written your final project up in HTML,
put it and your presentation slides in my directory.
On AcIS, I made
~es66/public_html/finalproj/ writable -- make a subdir with the last
name of a team member, and copy your stuff in there. Make the main
file called index.html (not .index.html).
ALSO, please do so for your temporal writeup (if it is
text or HTML) in /temporal/ (writeup and implementation!) and your NN
writeup, if it is in HTML, in /nn/ -- THANKS!
- DEADLINES: final presentations are during the final
exam slot, May 13 1:10-4. Final exam is take-home,
due May 10, under Eric's door, 703 CEPSR. Final
project writeup and final presentation are not due
until the presentation time slot. But you are strongly
advised to complete all implementation and/or experimentation
by May 3, after which you can focus on doing the final,
doing the final project writeup, and preparing the final
- If you want to do a project this summer or fall email projects@cs
or see www.cs.columbia.edu/~projects -- cc me for my info.
- FINAL PRESENTATIONS: May 13 THURS MUD 545 110 PM 400 PM
The final exam is take-home, given out last class, due may 10.
Final proj due may 13, but highly recommended that
all but writeup is done may 3
Course syllabus -- What happened
in the past; what is planned for the future.
Jeff's (the TA's) homepage for this course.
"Learn This" Rap (on machine induction)
HW1: Temporal inference
HW2: Neural Networks
(columbia.spring.cs4721-sec1) (Eric just added this - email
him and Jeff if it doesn't work.)
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 messed up.