Advanced Intelligent Systems (W4721)

General Information

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 final project).

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 ( Office hours: TBA office: 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.

Homework projects:

  1. Temporal constraint inference; application thereof
  2. Neural networks for image classification, e.g., face recognition
  3. Short oral presentation of a published research paper
  4. Final project (optionally can build on earlier homework)
Also, written work on probability will be included in HW2 and on the midterm.

The last two projects above facilitate learning about AI research, including:

Course Themes

Prerequisite: A course in artificial intelligence; or permission of the instructor

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.

Grading : 16% HW1, 16% HW2, 12% midterm, 8% paper presentation, 31% final project, 17% final exam.

Late homework: 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 encouraged. 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




OFFICIAL STUDENT PRESENTATION SCHEDULE -- ignore the one off jeff's page.

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

Class newsgroup (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.