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MS in Machine Learning

Carnegie Mellon University

Masters Program

Machine Learning ,

Department of Algorithms and Machine Learning


MS in Machine Learning at the prestigious Carnegie Mellon University is an opportunity for students to enhance their knowledge, skills, and career through innovation and research. Being a renowned university, Carnegie Mellon University receives enough funds to ensure the best education facilities for its students across all programs.

This post-graduate program offered on a full time basis has a well-designed curriculum that perfectly blends theory and practical learning. It prepares the students for their professional life to reach new heights of success.

MS in Machine Learning at CMU is ranked globally by estimated organisations. Such recognition speaks volumes about the course’s importance and effectiveness in the present scenario.

The top-notch faculty, modern facilities, and the aura of creativity and innovation in the CMU campus is a life-changing experience for the students looking forward to kickstarting or upgrading their careers. Overall, a MS in Machine Learning at Carnegie Mellon University is an excellent opportunity to grow into a learned professional and bring new developments in the world.


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Course Highlights

Program duration

24 Months

Degree type

Algorithms and Machine Learning



Delivery type

Full time

Delivery medium

On Campus

Tuition fees

waived flag Fees mentioned may differ from the actual amount.

Annual tuition fees


Full course fees (24 months)


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Application Fee


Application date

September 7, 2022


Application Deadline Fall

Dec 13, 2022


Application pre-requisites

  • Online Application
  • Application Fee
  • Transcripts 
  • Statement of purpose
  • GRE
  • Resume
  • Three Letters of Recommendation

Test score requirements

Minimum English Scores







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Yocketer profiles

Sathiya Sudhan

Sathiya Sudhan

Machine Learning


Spring 2020

Sanskaar Sehgal

Sanskaar Sehgal

Machine Learning


Fall 2021

Course Details

Core subject areas

  • Introduction to Machine Learning
  • Advanced Deep Learning
  • Probabilistic Graphical Models
  • Machine Learning in Practice
  • Convex Optimization

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