The Machine Learning Track

The Machine Learning Track is intended for students who wish to develop their knowledge of machine learning techniques and applications. Machine learning is a rapidly expanding field with many applications in diverse areas such as bioinformatics, fraud detection, intelligent systems, perception, finance, information retrieval, and other areas.

1. Overall Requirements

Students must complete a total of 30 credits:

  1. Fulfill the 12-credit core requirement.

  2. 2 required courses (6 credits): select 2 from among COMS W4771, COMS W4252, and COMS W4772 (E6772).

  3. 6 elective credits at the 6000-level, at least 3 of these 6000-level credits must be selected from the list of section 4.

  4. 6 credits of general elective graduate courses, at 4000 level or above; at least 3 of these credits must be CS graduate courses.

  5. Students using previous courses to fulfill core or track requirements may complete the 30 graduate credits by expanding their electives selected from (a) the list of required track courses; (b) the list of elective track courses; or (c) other graduate courses. At most 3 credits overall may be from non-technical graduate courses.

For the 12-credit core requirement, students take four courses from the following six:

COMS W4115 Programming Languages & Translators

COMS W4118 Operating Systems 1

COMS W4156 Advanced Software Engineering

CSOR W4231 Analysis of Algorithms 1

COMS W4701 Artificial Intelligence

CSEE  W4824 Computer Architecture

2. Pre-requisites

None.

3. Required Track Courses

Candidates are required to complete two (2) of the following courses*:

Course ID

Title

  Fall 2009** Spring
2010** 		 Fall 2010**

COMS W4252

Introduction to Computational Learning Theory

 Offered

 

 

COMS W4771

Machine Learning

 

 Offered

 

COMS W4772 (E6772) 

Advanced Machine Learning

 Offered

 

 


Students who have completed equivalent courses with grades of at least 3.0 may apply those courses to satisfy these requirements and devote more credits to pursue elective courses.

4. Elective Track Courses

Students are required to take two courses from the following list, at least one of which must be a 6000-level course. Other courses on this list may be used as general electives or to replace core or required track courses when the student has received a waiver.

Course ID

Title

Fall 2009**
 Spring
2010**		 Fall 2010**

COMS W4111

Introduction to Databases

 Offered

 Offered

 

COMS W4252

Introduction to Computational Learning Theory 

 Offered

 

 

COMS W4705

Intro to Natural Language Processing

 Offered

 Offered

 

COMS W4731

Computer Vision


 

 

COMS W4737

Biometrics

 Offered

 

 

COMS W4761

Computational Genomics

 

 Offered

 

COMS W4771

Machine Learning 

 

 Offered

 

COMS W4772 (E6772)

Advanced Machine Learning 

 Offered

 

 

COMS E6111

Advanced Database Systems

 

 Offered

 

COMS E6253

Advanced Topics in Computational Learning Theory

 

 

 

COMS E6717 (ELEN E6717)

Information Theory

 

 

 

COMS E6735

Visual Databases

 

 

 

COMS E6737

Biometrics

 

 

 

COMS E6901

Projects in Computer Science

 Offered

 Offered

 Offered

COMS E6998

Search Engine Technology

 

 

 

COMS E6998

Network Theory

 

 

 

COMS E6998

Algorithmic Game Theory

 

 

 

IEOR E6611

Semidefinite and Second-order cone programming/Optimization I

 

 

 

IEOR E8100
Optimization Methods in Machine Learning
 Offered  

SIEO 4150 or STAT W4201

Probability and Statistics/Advanced Data Analysis

 Offered

 

 

STAT G6101

Statistical Modeling and Data Analysis I

Offered

 

 

Please visit the Directory of Classes to get the updated course listings.

5. General Electives

Candidates are required to complete at least 6 additional graduate credits at, or above, the 4000 level; at least 3 of these credits must be CS, the other 3 credits may be a technical or non-technical elective approved by the track advisor. At most 3 credits overall of the 30 graduate credits required for the MS degree may be non-technical.

6. Contact

Please direct all questions concerning the Machine Learning Track to Prof. and Prof. Tony Jebara.

7. Graduation

Candidates preparing for graduation should submit a completed application for degree to the Registrar's Office and submit a track graduation form to C.S. Student Services (an example of a completed form is available here).


 

*Note: The list of electives may be updated to reflect changes in the schedule of course offerings.

**Please note that these course offerings are listed on a provisional basis only and may change from what is listed here.

*** ELEN-E4810 - Students who took it in Fall 06 or earlier can use it as an elective.

****IEOR E6613 - Students who took it in Fall 06 or earlier can use it as an elective. 

The Elective Track description updated on 9/28/2006. 

 

Last updated 7/23/09.