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:

  • Fulfill the 12-credit core requirement.

  • Students must take at least 12 credits total from Section 3 ("Required Track Courses") and 4 ("Elective Track Courses") below, including at least 6 credits from section 3 and at least 3 6000-level credits from Section 4.

  • Students must take at least 6 credits of technical courses at the 6000-level overall.

  • If the number of credits used to fulfill the above requirements is less than 30, then general elective graduate courses at 4000 level or above must be taken so that the total number of credits taken is 30. At least three of these credits must be CS Department graduate course.

  • 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.

2. Core Requirements

For the 12-credit core requirement, students take 4 courses from the following 6:

COMS W4115 Programming Languages & Translators
COMS W4118 Operating Systems
COMS W4156 Advanced Software Engineering
CSOR W4231 Analysis of Algorithms
COMS W4701 Artificial Intelligence
CSEE  W4824 Computer Architecture

3. Required Track Courses

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

Course ID

Title

COMS W4252

Introduction to Computational Learning Theory

COMS W4771

Machine Learning

COMS W4772 

Advanced Machine Learning


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

COMS W4111

Introduction to Databases

COMS W4252

Introduction to Computational Learning Theory 

COMS W4705

Intro to Natural Language Processing

COMS W4731

Computer Vision

COMS W4737

Biometrics

COMS W4761

Computational Genomics

COMS W4771

Machine Learning 

COMS W4772 

Advanced Machine Learning 

COMS W4995
Intro Social Networks

COMS E6111

Advanced Database Systems

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

COMS E6998

Search Engine Technology

COMS E6998

Network Theory

COMS E6998

Algorithmic Game Theory

COMS E6998
Statistical Methods for NLP
COMS E6998
NLP for the Web
COMS E6998
Advanced Topic in Machine Learning
COMS E6998
Machine Translation
COMS E6998Machine Learning for NLP
COMS E6998
Intro/Distributed Data Mining
COMS E6998
Analysis of social Info. Nets
COMS E6998
Algorithms/Deal/Massive Data
CSEE E6898
Large-Scale Machine Learning
CSEE E6898
Sparse Signal Modeling

IEOR E6613

Optimization I

IEOR E8100
Optimization Methods in Machine Learning

SIEO 4150 or STAT W4201

Probability and Statistics/Advanced Data Analysis

STAT W4240
Data Mining

STAT G6101

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. Please complete a non-tech approval form, and once it is signed, forward it to Janine Maslov or Remi Moss. At most 3 credits overall of the 30 graduate credits required for the MS degree may be non-technical. 

6. Track Planning

Please visit the Directory of Classes to get the updated course listings. If you would like to see how often the courses are offered, please visit the course page on the CS Department website.  

Not all courses are offered every semester, or even every year; a few courses are offered only once every two or three years or even less frequently. Among the core courses, 4115, 4118, 4701, and 4231 are normally offered every semester (fall and spring) but 4156 and 4824 are normally offered only one semester per year and which semester (fall vs. spring) may vary. Among the 4000-level track electives, only 4111 is normally offered every semester; none of the 6000-level track courses are offered every semester and some are not even offered every year. There are, however, typically one or more relevant 4995 and/or 6998 offerings each semester, and its generally possible to find a suitable 6901 project any semester. For more information, please see the SEAS Bulletin CS course-offering schedule (Please note that the course-offering schedule can change due to unforeseeable circumstances; thus, it should only be used as a reference).

7. Contact

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

8. 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. 

Last updated 5/23/2012.