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MSc Applied Machine Learning

Imperial College London

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Course options

  • Qualification

    MSc - Master of Science

  • Location

    South Kensington Campus

  • Study mode

    Full time

  • Start date

    01-OCT-22

  • Duration

    1 year

Course summary

During this course, you will focus on applying machine learning to electrical engineering. Applications include robotics, computer vision bio-inspired learning, communication and signal processing. This course is perfect if you’re interested in developing real-world systems. These will involve signals, sensors and hardware, such as robots or communication devices. Address the challenge of how automated systems can learn from signals and data as they operate in real environments. Learn through specialised modules including a specialised computer laboratory module. These allow you to develop practical skills with industrial input and your own individual project.

  • Learn the theoretical basis for machine learning systems, design methods and algorithms for modelling real systems
  • Equip yourself to a range of careers. These could involve the design, modelling, analysis and control of intelligent signal and data processing
  • Develop advanced knowledge of machine and deep learning in engineering
  • Gain skills that enable you to continue your studies to PhD level

Through the course, you will learn from field-leading staff. You will gain a critical awareness of current issues, research and their applications. Applying different methodologies, you will tackle complex issues systematically and creatively.

Tuition fees

Students living in United States
(international fees)

£ 36,500per year

Tuition fees shown are for indicative purposes and may vary. Please check with the institution for most up to date details.

University information

Imperial College London

  • University League Table

    5th

  • Campus address

    Imperial College London, South Kensington Campus, Kensington and Chelsea, SW7 2AZ, England

Subject rankings

  • Subject ranking

    3rd out of 114 1

  • Entry standards

    / Max 236
    214 91%

    4th

    5
  • Graduate prospects

    / Max 100
    98.0 98%

    4th

    1
  • Student satisfaction

    / Max 5
    4.14 83%

    5th

    13

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