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CS@CU Machine Learning
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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

COMS 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 2008**

 Spring 2008

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 2008**

 Spring 2008

COMS-W4111

Database Systems

Offered

 Offered

COMS-W4252 

Introduction to Computational Learning Theory Offered 

COMS-W4705

Intro to Natural Language Processing

Offered

 

COMS-W4731

Computer Vision

Offered

 
COMS-W4737
Biometrics
Offered  
 

COMS-W4761

Computational Genomics

 

 

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

 

Offered
COMS-E6735
Visual Databases
  Offered
COMS-E6737
Biometrics
 Offered
 

COMS-E6901

Projects in Computer Science

Offered

 Offered
COMS-E6998 Search Engine Technology
 
 
COMS-E6998 Network Theory
 Offered

IEOR-6611 or IEOR E4007

Convex Optimization/Optimization I

 

 

SIEO 4150 or STAT 4201

Probability and Statistics/Advanced Data Analysis

Offered 

 Offered

STAT-6101

Statistical Modeling and Data Analysis I

 

 

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. .

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 Fall 2008 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 3/13/2008.


Credits

Columbia University Department of Computer Science / Fu Foundation School of Engineering & Applied Science
450 Computer Science Building / 1214 Amsterdam Avenue, Mailcode: 0401 / New York, New York 10027-7003
Tel: 1.212.939.7000 / Fax: 1.212.666.0140

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