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

Machine Learning track students must complete a total of 30 points.

  • Machine Learning track requires:

    - Breadth courses
    - Required Track courses (6pts)
    - Track Electives (6pts)
    - General Electives (6pts)

  • Students must take at least 6 points of technical courses at the 6000-level overall. One of the Track Electives courses has to be a 3pt 6000-level course from the Track Electives list (Section 4)

  • 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 points must be CS graduate course.

  • Students using previous courses to fulfill track requirements may complete the 30 graduate points by expanding their electives selected from (a) the list of required track courses; (b) the list of Track Elective courses; or (c) other graduate courses.

2. Breadth Requirements

Students are required to satisfy Breadth Requirements by taking 1 course from Group 1, 1 course from Group 2, 1 course from Group 3, and 1 more course from any of the three groups.

Group
 Courses
Group 1 (Systems)

All CS 41xx courses except CS 416x and CS 417x
All CS 48xx courses, and CS 4340, 4444, and 4460

Group 2 (Theory)
All CS 42xx courses and COSR 42xx
Group 3 (AI and Apps)
All CS 47xx courses, and CS 416x and CS 417x

3. Required Track Courses

Students 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.

** These courses also can be used to satisfy the breadth requirements (Group 2 and Group 3).

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 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

Students are required to complete at least 6 additional graduate points at, or above, the 4000 level; at least 3 of these points must be CS, the other 3 credits may be non-CS/non-technical course approved by the track advisor. Candidates who wish to take a non-CS/non-Technical course should complete a non-tech approval form, get the advisor's signature, and submit it to Janine Maslov. At most 3 points overall of the 30 graduate points required for the MS degree may be non-CS/non-technical. 

6. Track Planning

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

Please also note that 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. For more information, please see the SEAS Bulletin CS course-offering schedule (This schedule can change due to unforeseeable circumstances; thus, it should only be used as a reference).

7. Track Advisor

Prof. and Prof. for Computer Science curriculum related questions.

8. Graduation

Candidates preparing for graduation should submit a completed application for degree to the Registrar's Office. Also submit a track graduation form to Janine Maslov by a specified date (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 her

Last updated 5/23/2012.