Computer Science Master's Degree - Machine Learning

Machine Learning

Online Program Overview

Degree Level
Master's Degree
Total Credits
Fully Online
Contact Us
Minimum GPA
Qualifying Exam
GRE Required

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.

"CVN has allowed a single father like me to continue my education as well as provide my child with quality care. Hats off to the CVN faculty."
Dave Martin, Sikorsky Aircraft Corporation

Admissions Requirements

Degree required for admission: Most candidates have completed an undergraduate degree in computer science. Applicants with degrees in other disciplines and a record of excellence are encouraged to apply; these applicants are required to have completed at least six prerequisites: 4 computer science courses covering the foundations of the field and 2 math courses. 

The four computer science courses can be ANY undergraduate/graduate-level computer science course. Examples of computer science courses are listed below, but applicants can also take courses in computer languages such as R, Python, C++...etc. As. long as the course is coded as a CS course on your college transcript, the course will meet the CS requirement.

Examples of computer science courses:

  • Intro to Computer Science (COMS W1004 or COMS W1007)
  • Advanced Programming (COMS W3157)
  • Data Structures and Algorithms (COMS W3134 or W3137), which is a prerequisite for most of our graduate-level courses
  • Discrete Math (COMS W3203)
  • Must be grade- and credit-bearing and issued on a university transcript

Math prerequisites:

  • Linear Algebra
  • Differential Equations
  • Must be grade- and credit-bearing and issued on a university transcript

Please note that the six courses must be taken at a university (can be online), but MOOCs such as courses on Coursera and edX do NOT meet this requirement. These courses are not offered here at Columbia Video Network, but may be taken at your local university.  Work experience does not waive this requirement. 

GPA required: Most students admitted have earned a grade point average above 3.5 (out of 4.0); a GPA of at least 3.3 is required. 

GRE requirements: General test is optional for Spring, Summer, and Fall 2022. 

Competence in English: Applicants who earned their undergraduate/graduate degree in a country other than Australia, Canada, Ireland, New Zealand, Singapore, the UK, and the United States of America must submit an official score from an approved English language proficiency exam. Approved English language proficiency exams are the TOEFL,  IELTSPTE Academic test, or Duolingo English Test. This requirement applies to applicants from Bangladesh, Nepal, India, Pakistan, Latin America, the Middle East, Israel, the People’s Republic of China, Japan, Korea, Southeast Asia, most European countries, and most countries in Africa.

Other application requirements: 3 recommendation letters, transcripts, resume, and a personal-professional statement are required. All application requirements in the Graduate Application must be completed as specified in the application. For answers to your most common admissions questions, please review our FAQ page here.

To apply, click here

Overall Requirements

Students must complete at least 30 points of graduate coursework as outlined below.

Machine Learning track requires:

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

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.

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.



Students must complete all core courses and selected electives for a total of 30 graduate points of academic work via CVN while maintaining a minimum grade point average of 2.7. All degree requirements must be completed within 5 years of the beginning of the first course credited toward the degree. This includes courses taken in the non-degree program.


Course List

For the most up-to-date course information, visit the CS Machine Learning page.

Breadth Requirement

All students must complete the Breadth Requirement. Visit the breadth requirement page for a list of courses.

Required Track Courses

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.

Group A

  • COMS W4252: Introduction to Computational Learning Theory
  • COMS W4771: Machine Learning*
  • COMS W4721: Machine Learning for Data Science*
  • COMS W4772: Advanced Machine Learning
  • COMS/STAT G6509/6701: Foundations of Graphical Models 

Group B

  • COMS W4731: Computer Vision
  • COMS W4705: Natural Language Processing
  • COMS W4733: Computational Aspects of Robotics
  • COMS W4701: Artificial Intelligence

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. 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.

  • COMS W4111: Database Systems
  • 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: Machine Learning*
  • COMS W4721: 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 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 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
  • ECBM E4040: Neural Netowrks and Deep Learning
  • ECBM E6040: Neural Networks and Deep Learning Research
  • EECS E6720: Bayesian Models of Machine 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
  • ELEN E6886: Sparse Representations and Higher Dimensional Geometry
  • IEOR E4150: Probability and Statistics (formerly SIEO W4150)
  • IEOR E6613: Optimization I
  • IEOR E8100: Optimization Methods in Machine Learning
  • IEOR E8100: Big Data & Machine Learning
  • IEOR E8100: Reinforcement Learning
  • MECS E6615: Advanced Robotic Manipulation

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.

* 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).

Tuition & Fees

2023–2024 Tuition & Fees

Please note that all tuition and fees are in U.S. dollars and are estimated. Tuition and most fees are prescribed by statute and are subject to change at the discretion of the Trustees.

CVN Credit Tuition: $2,462.00 per point (Credit Hour)
CVN Fee: $395 non-refundable fee per course
Transcript Fee: $105 non-refundable one-time fee
Tuition Deposit: $1,000 (More information on our Resources page)

Estimated cost of one nondegree course: $7,886.00
Estimated total cost of certification (four courses): $31,229.00

Estimated total cost of MS (ten courses): $77,915.00
*Estimated total cost of DES (ten courses plus a minimum of 12 research credits): $108,249.00

Graduate Admission Application Fee: $85 non-refundable one-time fee 
Certification Program Application Fee: $85 non-refundable one-time fee 

Late Registration Fee: $100 non-refundable fee

CVN Withdrawal Fee: $75, plus prorated tuition and all non-refundable fees
For example: A three-credit course would be $7,781 + transcript fee $105 (one-time) + CVN fee $395 = $7,886

*Assumes DES student enrolls in two six-credit research courses.

For Drop/Withdrawal fees and dates, refer to the Academic Calendar for the current term.

Please note: CVN no longer offers courses for audit.


Payment should be mailed to:

Columbia University
Student Account Payments
P.O. Box 1385
New York, NY 10008-1385

Before you mail your check or money order, please take careful note of the following requirements to ensure the timely processing of your payment:

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