Foundations of Machine Learning for Scientists and Engineers
University of Ottawa · ON · IAI5101
Foundations of Machine Learning for Scientists and Engineers is a subject at University of Ottawa, ON. It covers topics like deep-learning, machine-learning, software-fundamentals, statistical-learning. It is part of 6 degrees. The information below is compiled from public course material — approximate, not official.
University of Ottawa
IAI5101
3.00
Topics covered
Degrees that include Foundations of Machine Learning for Scientists and Engineers
- Master of Applied Science Electrical and Computer Engineering
- Master of Applied Science in Systems Engineering
- Master of Computer Science
- Master of Engineering Electrical and Computer Engineering
- Master of Engineering in Systems Engineering
- Master of Interdisciplinary Artificial Intelligence (Online)
Frequently asked questions
What is Foundations of Machine Learning for Scientists and Engineers about?
Foundations of Machine Learning for Scientists and Engineers covers topics like deep-learning, machine-learning, software-fundamentals, statistical-learning.
Which degrees include Foundations of Machine Learning for Scientists and Engineers?
Foundations of Machine Learning for Scientists and Engineers is part of 6 degrees, including Master of Applied Science Electrical and Computer Engineering, Master of Applied Science in Systems Engineering, Master of Computer Science, Master of Engineering Electrical and Computer Engineering, Master of Engineering in Systems Engineering.
How many credit points is Foundations of Machine Learning for Scientists and Engineers?
Foundations of Machine Learning for Scientists and Engineers at University of Ottawa is typically around 3.00 credit points. Approximate — confirm with the university.
Where is Foundations of Machine Learning for Scientists and Engineers taught?
Foundations of Machine Learning for Scientists and Engineers is taught at University of Ottawa, ON.
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Sources: University of Ottawa public course catalogue. Information is compiled and summarised — not copied verbatim — and is approximate; always confirm with the original source.
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