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COMPX310-23B (TGA)
Machine Learning
15 Points
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The University of Waikato
Academic Divisions
Division of Health Engineering Computing & Science
School of Computing and Mathematical Sciences Office
Department of Computer Science
Staff
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Convenor(s)
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Bernhard Pfahringer
bernhard.pfahringer@waikato.ac.nz
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Nick Lim
9011
FG.2.07
nick.lim@waikato.ac.nz
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Administrator(s)
: buddhika.subasinghe@waikato.ac.nz
Librarian(s)
: anne.ferrier-watson@waikato.ac.nz
You can contact staff by:
- Calling +64 7 838 4466 select option 1, then enter the extension.
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Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
- For extensions starting with 4: dial +64 7 838 extension.
- For extensions starting with 5: dial +64 7 858 extension.
- For extensions starting with 9: dial +64 7 837 extension.
- For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
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What this paper is about
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This paper introduces Machine Learning which is the science of making predictions. ML algorithms strive to be fast and highly accurate, while processing large datasets. This paper will use standard Python-based ML toolkits to teach the fundamentals of ML.
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How this paper will be taught
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Two lectures per week, that are live-streamed on Zoom, and recorded, plus a weekly assignment that you can get help with in the lab or on Zoom, at specific hours.
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Required Readings
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Learning Outcomes
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Students who successfully complete the course should be able to:
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Assessments
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How you will be assessed
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Please note that all assessment dates and times below are subject to change. The Moodle/elearn settings will be the correct ones. Make sure to check them.
If you are enrolled in a BE(Hons), samples of your work may be required as part of the Engineering New Zealand accreditation process for BE(Hons) degrees. Any samples taken will have the student name and ID redacted. If you do not want samples of your work collected then please email the engineering administrator, Natalie Shaw (natalie.shaw@waikato.ac.nz), to opt out.
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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam.
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