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.


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.


Visit the breath requirement page for more information.


Students must complete two required track courses by either taking two courses from group A, or one course from group A plus one course from group B. (At least one course must be taken from group A). 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


A COMS W4252 Introduction to Computational Learning Theory
A COMS W4771 or COMS W4721* Machine Learning/Machine Learning for Data Science
A COMS W4772 Advanced Machine Learning
A COMS/STAT G6509/6701 Foundations of Graphical Models (This course is an advanced course, but MS students may register for it with instructor approval)
B COMS W4731 Computer Vision
B COMS W4705 Natural Language Processing
B COMS W4733 Computational Aspects of Robotics
B COMS W4701 Artificial Intelligence

* 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).


Students are required to take 2 courses from the following list, at least one of which must be a 6000-level course. Student cannot ‘double count’ a course that they took as a required track course as a track elective. 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 Biometrics
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 Biometrics
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
STAT GR8101 Topics in Applied Statistics: Applied Causality

*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.


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.


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


Please direct all questions concerning the Machine Learning track to Prof. David Blei, Prof. Daniel Hsu, and Prof. Tony Jebara.


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.