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.

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 6000level overall. One of the Track Electives courses has to be a 3pt 6000level course from the Track Electives list.

If the number of points used to fulfill the above requirements is less than 30, then General Elective graduate courses at 4000level or above must be taken so that the total number of credits taken is 30.

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 
Title 
COMS W4252 
Introduction to Computational Learning Theory 
COMS W4771 or COMS W4721* 
Machine Learning/Machine Learning for Data Science 
COMS W4772 
Advanced Machine Learning 
* 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 6000level 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 
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 
Intro Social Networks 
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 
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 E6998 
Machine Learning for NLP 
COMS E6998 
Intro/Distributed Data Mining 
COMS E6998 
Analysis of social Info. Nets 
COMS E6998 
Algorithms/Deal/Massive Data 
COMS E6998 
Econ of Social Networks 
COMS E6998 
Social Networks 
COMS E6998 
CV and ML an Mobile Platforms 
COMS E6998 
Data Science & Entrepreneurship 
COMS E6998* 
Fund of Speaker Recognition/Fund of Speech Recognition 
COMS E6998 
Bayesian Analysis for NLP 
COMS E6998 
Sublinear Time Algos Learning 
COMS E6998 
Semantic Tech in IBM Watson 
COMS E6998 
Cloud and Big Data 
COMS E6998 
Digitally Mediated Storytelling 
COMS E6998 
HighDimensional Data Analysis 
COMS E6998 
Experimental Analysis Algos 
COMS E6998 
Comp Models of Social Meaning 
CSEE E6892 
Bayesian Models in Machine Learning 
CSEE E6898 
LargeScale Machine Learning 
CSEE E6898 
Sparse Signal Modeling 
APMA E4990 
Modeling Social Data 
EEBM E6040 
Neural Networks and Deep Learning 
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 
SIEO 4150 or STAT W4201 
Probability and Statistics/Advanced Data Analysis 
STAT W4240 
Data Mining 
STAT W4249 
Applied Data Science 
STAT G4400 
Statistical Machine Learning 
STAT W4640 
Bayesian Statistics 
STAT W4700 
Probability and Statistics 
STAT G6101 
Statistical Modeling and Data Analysis I 
STAT G6104 
Computational Statistics 
* Due to significant overlap, students can receive credits for only one of these courses.
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 nonCS/nontechnical course approved by the track advisor. Candidates who wish to take a nonCS/nonTechnical course should complete a nontech 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 nonCS/nontechnical.
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 courseoffering 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.
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.