Computer Science Master's Degree - Machine Learning
Machine Learning
Online Program Overview
Minimum GPA
Qualifying Exam
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.
Requirements
- Complete a total of 30 points (Courses must be at the 4000 level or above)
- Maintain at least a 2.7 overall GPA (No more than one D is permitted)
- Satisfy breadth requirements
- Take at least six points of technical courses at the 6000 level
- At most up to three points of your degree can be Non-CS/Non-track If they are deemed relevant to your track and sufficiently technical in nature. Submit the Non-CS/NonTrack form and the course syllabus to your CS Faculty Advisor for review
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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: four computer science courses covering the foundations of the field and two 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.
Example 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 Massive Open Online Courses (MOOCs) such as courses on Coursera and edX do NOT meet this requirement. These courses are not offered here at Columbia Video Network, but they may be taken at your local university. Work experience does not waive this requirement.
GPA requirements: 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: the GRE is optional, but it is not required.
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, IELTS, PTE 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: three recommendation letters, transcripts, a resume, and a personal professional statement are required. All application requirements in the Graduate Application must be completed as specified in the application.
We accept applications on a rolling basis, which means there are no deadlines to apply. Please submit your degree applications no later than 4–6 weeks before the semester you wish to enroll.
For answers to your most common admissions questions, please review our Frequently Asked Questions page. To apply, visit the Application Process page.
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 2.7 overall grade point average. No more than one D is permitted. All degree requirements must be completed within five 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, please visit the Columbia Engineering Computer Science Machine Learning page.
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: $2,584.00 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: $8,252.00
Estimated total cost of certification (four courses): $32,693.00
Estimated total cost of MS (ten courses): $81,575.00
*Estimated total cost of DES (ten courses plus a minimum of 12 research credits): $112,583.00
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 $7,752 + transcript fee $105 (one-time) + CVN fee $395 = $8,252
*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:
Attn: Shewanna House
Accounting & Business Management
Senior Student Services Rep
Payments & Deposits
210 Kent Hall, Mail Code 9205
1140 Amsterdam Avenue
New York, NY 10027
Before you mail your check or money order, please take careful note of the following requirements to ensure the timely processing of your payment: https://sfs.columbia.edu/content/pay-mail.
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