Machine Learning with a Year in Industry
Royal Holloway, University of London · England · MACHINE-LEARNING-WITH-A-YEAR-IN-INDUSTRY
Machine Learning with a Year in Industry is a Postgraduate taught degree at Royal Holloway, University of London, England, typically 2 years of full-time study. It covers areas like machine learning, data analytics, data mining, professional experience, data science, business. The summary below is compiled from public course information — approximate, not official.
Royal Holloway, University of London
Postgraduate taught
2 years
£14,900
What you'll study in Machine Learning with a Year in Industry
Example subjects: Year in Industry, Year in Industry, Extramural Year, Individual Project in Artificial Intelligence, Machine Learning, Deep Learning, Deferred Year in Industry, Online Machine Learning, Computer Science (Artificial Intelligence) MSci Project, Deep Learning, Artificial Intelligence Principles and Techniques, Introduction to Reinforcement Learning.
Frequently asked questions
How long is Machine Learning with a Year in Industry?
Machine Learning with a Year in Industry at Royal Holloway, University of London is typically 2 years of full-time study. Approximate — confirm with the university.
What will I study in Machine Learning with a Year in Industry?
Machine Learning with a Year in Industry covers areas like machine-learning, deep-learning, data-mining, data-analytics, professional-experience, data-science, statistical-learning, research-projects.
How much does Machine Learning with a Year in Industry cost?
Indicative domestic fees for Machine Learning with a Year in Industry are around £14,900 (approximate, usually per year). International fees differ — confirm with the university.
Who offers Machine Learning with a Year in Industry?
Machine Learning with a Year in Industry is offered by Royal Holloway, University of London, England.
Considering Machine Learning with a Year in Industry?
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Sources: Royal Holloway, University of London public course catalogue. Information is compiled and summarised — not copied verbatim — and is approximate; always confirm with the original source.
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