Introduction to Machine Learning
Massachusetts Institute of Technology · MA · 6.3900
Introduction to Machine Learning is a subject at Massachusetts Institute of Technology, MA. It covers topics like data-mining, deep-learning, machine-learning, statistical-learning. It is part of 12 degrees. The information below is compiled from public course material — approximate, not official.
Massachusetts Institute of Technology
6.3900
12.00
Topics covered
Degrees that include Introduction to Machine Learning
- Artificial Intelligence and Decision Making (Course 6-4)
- Brain and Cognitive Sciences (Course 9)
- Business Analytics (Course 15-2)
- Climate System Science and Engineering (Course 1-12)
- Computation and Cognition (Course 6-9)
- Computer Science and Molecular Biology (Course 6-7)
- Computer Science, Economics, and Data Science (Course 6-14)
- Earth, Atmospheric, and Planetary Sciences (Course 12)
- EECS Tracks
- Linguistics and Philosophy (Course 24-2)
- Mathematics with Computer Science (Course 18-C)
- Urban Science and Planning with Computer Science (Course 11-6)
Frequently asked questions
What is Introduction to Machine Learning about?
Introduction to Machine Learning covers topics like data-mining, deep-learning, machine-learning, statistical-learning.
Which degrees include Introduction to Machine Learning?
Introduction to Machine Learning is part of 12 degrees, including Artificial Intelligence and Decision Making (Course 6-4), Brain and Cognitive Sciences (Course 9), Business Analytics (Course 15-2), Climate System Science and Engineering (Course 1-12), Computation and Cognition (Course 6-9).
How many credit points is Introduction to Machine Learning?
Introduction to Machine Learning at Massachusetts Institute of Technology is typically around 12.00 credit points. Approximate — confirm with the university.
Where is Introduction to Machine Learning taught?
Introduction to Machine Learning is taught at Massachusetts Institute of Technology, MA.
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Sources: Massachusetts Institute of Technology public course catalogue. Information is compiled and summarised — not copied verbatim — and is approximate; always confirm with the original source.
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