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MSc in Machine Learning for Visual Data Analytics at Queen Mary, University of London (QMUL)

London , United Kingdom

Pursue a 12 Months PG Degree with INR 3,100,000/Year Tuition Fees

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MSc in Machine Learning for Visual Data Analytics at Queen Mary, University of London (QMUL)
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MSc in Machine Learning for Visual Data Analytics at Queen Mary, University of London (QMUL)

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Overview

The School of Electronic Engineering and Computer Science at Queen Mary University of London (QMUL) offers a Master's degree in Machine Learning for Visual Data Analytics. This one-year program focuses on IT and Software, making it a great choice for students interested in these fields. For international students, the tuition fee is £28,900. To apply for this degree, students need to meet the English language requirements, which can be fulfilled by taking either the IELTS or PTE exams. Additionally, applicants must have a graduation score of at least 60% to be considered for admission.

Program Details Information
Duration 12 Months
Course Level PG Degree
Mode of Course Full Time
Tution And Fees ( For 1 Year) INR 3100000.0
Type of University Public
Official Career service Website https://www.qmul.ac.uk/

MSc in Machine Learning for Visual Data Analytics at Queen Mary, University of London (QMUL)

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Key Insights

  • list items This course helps students learn about the latest technologies in machine learning for visual analytics. It gives them the background and skills needed for jobs in research or industry.
  • list items The program is taught by teachers from the Computer Vision and Multimedia and Vision research groups.
  • list items This program is made to prepare engineers for jobs that involve analyzing and understanding images and videos.

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    MSc in Machine Learning for Visual Data Analytics at Queen Mary, University of London (QMUL)

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    Rankings

    Ranking Source Ranking
    PG Computer Science THE (Times Higher Education), 2024 99
    Mathematics QS, 2024 110
    Physics ARWU (Shanghai Ranking), 2023 151

    MSc in Machine Learning for Visual Data Analytics at Queen Mary, University of London (QMUL)

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    Eligibility Criteria

    12th Grade

    No specific cutoff mentioned.

    Graduation

    Applicants need a 2:1 or higher at the undergraduate level in Electronic Engineering, Computer Science, Mathematics, or a related field. For India, the university looks for a Bachelor Degree (minimum 3 years) from selected institutions. The UK degree classifications are: First Class (70% to 80%), 2:1 (60% to 70%), and 2:2 (50% to 60%). Offer conditions may vary based on the institution.

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    IELTS

    Marks: 6.5

    Overall score of 6.5, with at least 6.0 in Writing and 5.5 in Reading, Listening, and Speaking.

    PTE

    Marks: 71.0

    Overall score of 71, including 65 in Writing, and 59 in Reading, Listening, and Speaking.

    Exams Accepted Qualifying Score
    IELTS 6.5
    PTE 71.0

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    MSc in Machine Learning for Visual Data Analytics at Queen Mary, University of London (QMUL)

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    Application Process

    Application Process for Queen Mary University of London

    Step 1: Prepare Your Documents

    • list items Statement of Purpose (SOP): You need to write a statement of purpose as part of your application.
    • list items Letters of Recommendation (LOR): Please provide the contact details of one or two referees. If you are currently studying or have graduated in the last five years, you should include an academic referee. If you graduated more than five years ago, you can include professional references.
    • list items Curriculum Vitae (CV): A CV is required for your application.

    Step 2: Additional Documents Required

    • list items Make sure to check if there are any other specific documents needed for your course. This may include transcripts, proof of English proficiency, or other relevant certificates.

    Step 3: Start Your Online Application

    Visit the following link to start your application: Queen Mary University Application.

    Step 4: Application Deadline

    The deadline for Fall (September) is September 2nd. Please note that these dates are tentative and based on previous records. For the exact deadline, you can connect with our expert counselors for assistance.