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.

Summary of Requirements

Machine Learning track students must complete a total of 30 points and must maintain at least 2.7 overall GPA in order to be eligible for the MS degree in Computer Science.

  1. Machine Learning track requires:

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

  2. 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.

  3. If the number of points 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.

  4. 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.

Please use the Degree Progress Check to keep track of your requirements.

1. Breadth Requirement

Visit the breath requirement page for more information. 

2. Required Track Courses

Students are required to complete 2 of the following courses. Students who have taken equivalent courses in the past and received grades of at least a B may apply for waivers and take other CS courses instead.

Course ID


COMS W4252

Introduction to Computational Learning Theory

COMS W4771 or COMS W4721*

Machine Learning/Machine Learning for Data Science

COMS W4772

Advanced Machine Learning


Foundations of Graphical Models (This course is an advanced course, but MS students may register for it with instructor approval)

* Due to significant overlap, students can receive credits for only one of these courses (either COMS W4771 Machine Learning or COMS W4721 Machine Learning for Data Science).

3. Elective Track Courses

Students are required to take 2 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


COMS W4111

Introduction to Databases

COMS W4252

Introduction to Computational Learning Theory 

CSOR W4246

Algorithms for Data Science

COMS W4705

Intro to Natural Language Processing

COMS W4731

Computer Vision

COMS W4733

Computational Aspects of Robotics

COMS W4737


COMS W4761

Computational Genomics

COMS W4771 or COMS W4721*

Machine Learning/Machine Learning for Data Science

COMS W4772

Advanced Machine Learning 

COMS W4776

Machine Learning for Data Science

COMS W4995

Visit the topics courses page to see which COMS 4995 courses apply to this track.

COMS E6111

Advanced Database Systems

COMS E6232

Analysis of Algorithms II

COMS E6253

Advanced Topics in Computational Learning Theory

COMS E6717 (ELEN E6717)

Information Theory

COMS E6735

Visual Databases

COMS E6737


COMS E6901

Projects in Computer Science

COMS E6998

Visit the topics courses page to see which COMS 6998 courses apply to this track.

CSEE E6892

Bayesian Models in Machine Learning

CSEE E6898

Large-Scale Machine Learning

CSEE E6898

Sparse Signal Modeling

APMA E4990

Modeling Social Data

BINF G4006

Translational Bioinformatics

EEBM E6040

Neural Networks and Deep Learning

EECS E6870

Speech Recognition

EECS E6893

Big Data Analytics

EECS E6895

Topic Adv Big Data Analytics

EECS E6894

Deep Learning for Computer Vision and Natural Language Processing

IEOR E6613

Optimization I

IEOR E8100

Optimization Methods in Machine Learning

IEOR E8100

Big Data & Machine Learning

MECS E6615

Advanced Robotic Manipulation

SIEO 4150/STAT 4001 or STAT W4201/4291/5291

Probability and Statistics/Advanced Data Analysis

STAT W4240*

Data Mining

STAT W4249

Applied Data Science

STAT G4400/4241/5241*

Statistical Machine Learning

STAT W4640/4224/5224

Bayesian Statistics

STAT W4700

Probability and Statistics

STAT G6101

Statistical Modeling and Data Analysis I

STAT G6104

Computational Statistics

*Due to a significant overlap in course material, students in the Machine Learning track can only take 1 of the following courses - ELEN 4903, IEOR 4525, STAT 4240, STAT 4400/4241/5241 - as a track elective or a general elective. 

4. 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 points 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 approval, and submit it to CS Student Services. At most 3 points overall of the 30 graduate points required for the MS degree may be non-CS/non-technical. 

5. 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).

Please note that some Data Science Institute courses such as COMS W4121 (Computer Systems for Data Science) do not count towards the CS MS degree. If you have any questions, please contact your advisor or the CS Student Services.

As of Spring 15, STAT W4252 Introduction to Data Science is no longer an approved track elective course.

** Known Non-Tech Course**

CSOR E4995 Financial Software Systems

6. Track Advisors

Please direct all questions concerning the Machine Learning track to Prof. , Prof. , and 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 CS Student Services.

Last updated 4/21/2016.