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SUMMARY OF REQUIREMENTS

Students must complete at least a total of 30 graduate credits and must maintain at least a 2.7 overall GPA in order to be eligible for the MS degree in Computer Science.

1. Breadth Courses (12 pts)

Visit the breadth requirement page for more information.

2. Required Track Courses (6pts)

Students must complete two required track courses by either taking two courses from group A or one course from group A and 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.

Group	Course ID	Title
A	COMS W4252	Introduction to Computational Learning Theory
A	COMS W4771 or COMS W4721 or ELEN 4720[1]	Machine Learning OR Machine Learning for Data Science OR Machine Learning for Signals, Information and Data
A	COMS W4772 or COMS 6772	Advanced Machine Learning
A	COMS 4995	Neural Networks Deep 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)
A	COMS 4732	Computer Vision II
A	COMS 4773	Machine Learning Theory
A	COMS 4774	Unsupervised Learning
A	COMS 4775	Causal Inference (Previously listed as COMS 4995: Causal Inference)
B	COMS W4731	Computer Vision I
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 the following courses- COMS W4771: Machine Learning, COMS W4721: Machine Learning for Data Science, OR (as of Spring 2020) ELEN 4720: Machine Learning for Signals, Information, and Data)

3. Track Electives (6pts)

Students are required to take 2 courses from the following list, at least one of which must be a 6000-level course. Students 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	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 4732	Computer Vision II
COMS W4733	Computational Aspects of Robotics
COMS W4737	Biometrics
COMS W4761	Computational Genomics
COMS E4762	Machine Learning for Functional Genomics
COMS W4771 or COMS W4721 or ELEN 4720[1]	Machine Learning OR Machine Learning for Data Science OR Machine Learning for Signals, Information and Data
COMS W4772 or COMS 6772	Advanced Machine Learning (or COMS 6998: Machine Learning Personalization only valid if taken in Spring 2018)
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 (Advisor approval required)
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
ECBM E4040	Neural Networks and Deep Learning
ECBM E6040	Neural Networks and Deep Learning Research
EECS E6691	Topics in  Data-Driven Analysis & Comp: Advanced Deep Learning
EECS E6699	Topics in Data-Driven Analysis and Computation: Mathematics of Deep Learning
EECS E6720	Bayesian Models of Machine Learning
EECS E6870	Speech Recognition
EECS E6893	Big Data Analytics or Topics-Information Processing (May only count 1 of these)
EECS E6895	Topic Adv Big Data Analytics
EECS E6894	Deep Learning for Computer Vision and Natural Language Processing
ELEN 6885	Reinforcement Learning
ELEN E6886	Sparse Representations and Higher Dimensional Geometry
ELEN E6899	Topics in Information Processing: Autonomous Multi-Agent Systems
IEOR E6613	Optimization I
IEOR 6617	Machine Learning and High-Dimensional Data
IEOR E8100	Optimization Methods in Machine Learning
IEOR E8100	Big Data & Machine Learning
MECS E6615	Advanced Robotic Manipulation
STAT 4001 (previously known as SIEO 4150)	Introduction to Probability and Statistics
STAT W4201/4291/5291 or IEOR 4150	Probability and Statistics/Advanced Data Analysis
STAT W4240* or IEOR 4540	Data Mining
STAT W4282 or STAT GU4205	Linear Regression/Time Series Analysis/Linear Regression Models
STAT W4249/STAT 4243	Applied Data Science
STAT G4400/4241/5241*	Statistical Machine Learning
STAT W4640/4224/5224	Bayesian Statistics
STAT 5242	Advanced Machine Learning
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 significant overlap, please be aware of the following exceptions:

• Students can receive credits for only one of these courses (COMS W4771: Machine Learning, COMS W4721: Machine Learning for Data Science, or ELEN 4720: Machine Learning for Signals, Information, and Data
• 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 (6pts)

Students must complete six credits of General Elective Courses, at the 4000 level or above. At least three of these points must be chosen from either the Track Electives listed above or from the CS department at the 4000 level or higher.

Students may also request to use at most 3 points of Non-CS/Non-Track coursework if approved by the process listed below.

• Non-CS/Non-Track: CS MS students may request up to 3 points of Non-CS/Non-Track points to count toward their 30-point MS program. CS Track advisor may review and approve if the course is determined to be relevant to the CS MS track and sufficiently technical in nature. Students should send the Non-CS/Non-Track Form and the course syllabus to your Track Advisor for review.

Please note:

• Students who waive track requirements by using previous courses must still complete 30 graduate credits. This can be done by expanding their elective selection to include courses listed as required track courses and elective track courses; or by taking other graduate courses

• 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

• 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

• The Degree Progress Checklist should be used to keep track of your requirements. If you have questions for your Track Advisor or CS Advising, you should have an updated Checklist prepared

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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.

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 CS Advising.

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

** Known Non-Track Course**

CSOR E4995: Financial Software Systems

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Updated: 07/25/2023