Introduction to Machine Learning
Australian National University · ACT · COMP6670
Introduction to Machine Learning is a subject at Australian National University, ACT in School of Computing. It covers topics like artificial-intelligence, deep-learning, machine-learning. It is part of 12 degrees. The information below is compiled from public course material — approximate, not official.
Australian National University
COMP6670
6.00
School of Computing
Topics covered
Degrees that include Introduction to Machine Learning
- Graduate Diploma of Computing
- Master of Actuarial Practice
- Master of Actuarial Studies
- Master of Engineering in Electrical Engineering
- Master of Machine Learning and Computer Vision
- Master of Science (Advanced) in Quantitative Biology and Bioinformatics
- Master of Science (Advanced) in Quantum Technology
- Master of Science in Agricultural Innovation
- Master of Science in Quantitative Biology and Bioinformatics
- Master of Science in Quantum Technology
- Master of Statistical Data Analysis
- Master of Statistics
Frequently asked questions
What is Introduction to Machine Learning about?
Introduction to Machine Learning covers topics like artificial-intelligence, deep-learning, machine-learning.
Which degrees include Introduction to Machine Learning?
Introduction to Machine Learning is part of 12 degrees, including Graduate Diploma of Computing, Master of Actuarial Practice, Master of Actuarial Studies, Master of Engineering in Electrical Engineering, Master of Machine Learning and Computer Vision.
How many credit points is Introduction to Machine Learning?
Introduction to Machine Learning at Australian National University is typically around 6.00 credit points. Approximate — confirm with the university.
Where is Introduction to Machine Learning taught?
Introduction to Machine Learning is taught at Australian National University, ACT.
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Sources: Australian National University public course catalogue. Information is compiled and summarised — not copied verbatim — and is approximate; always confirm with the original source.
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