MS

Computer Science Master's Degree - Machine Learning

Machine Learning

Online Program Overview

Degree Level
Master's Degree
Total Credits
30
Delivery
Fully Online
Contact Us
Minimum GPA
3.3
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 4 computer science courses covering the foundations of the field and 2 math courses. 

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 required. A subject GRE test is not required but may be helpful in strengthening your application. 

Competence in English: Applicants whose native language is not English and who have not studied at schools where English is the primary language must complete the TOEFL exam with a minimum grade of 600 on the written TOEFL, or 250 on the computerized version. 

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. 

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.

 

Description

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

Required Track Courses

Students are required to complete two (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.

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

Elective Track Courses

Students are required to complete two courses (6 points) from the following list; at least one course must be a 6000 level course. Other courses on this list may be used as general or to replace core or required track courses when the student has received a waiver.

  • COMS W4111: Database Systems
  • COMS W4252: Introduction to Computational Learning Theory
  • 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 OR 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 W4150: Probability and Statistics

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.

TRACK ADVISORS: 

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

Tuition & Fees

2017 - 2018 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: $1,936 per point (Credit Hour)
CVN Fee: $395 non-refundable fee per course
Transcript Fee: $105 non-refundable one-time fee

CVN Audit Tuition: $834 per point (Credit Hour)
Graduate Admission Application Fee: $150 non-refundable one-time fee
CVN Fee: $395 non-refundable fee per course

Certification Program Application Fee: $150 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 $5,808 + transcript fee $105 (one-time) + CVN fee $395 = $6,308

 

Payment should be mailed to:

Columbia Video Network
540 S.W. MUDD Building, MC4719
500 West 120th Street
New York, NY 10027

Interested in this program?

Request information to learn more about this program or bookmark it to come back later.

Request Info