
COMPX310-22B (TGA)
Machine Learning
15 Points
Staff
Convenor(s)
Bernhard Pfahringer
4041
G.2.23
bernhard.pfahringer@waikato.ac.nz
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Nick Lim
9011
FG.2.07
nick.lim@waikato.ac.nz
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Paper Description
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.
The learning outcomes for this paper are linked to Washington Accord graduate attributes WA1-WA11. Explanation of the graduate attributes can be found at: https://www.ieagreements.org/
Paper Structure
Learning Outcomes
Students who successfully complete the paper should be able to:
Assessment
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.
Assessment Components
The internal assessment/exam ratio (as stated in the University Calendar) is 50:50. The final exam makes up 50% of the overall mark.
Required and Recommended Readings
Required Readings
Recommended Readings
Online Support
Workload
Linkages to Other Papers
Prerequisite(s)
Prerequisite papers: (COMPX101 or ENGEN103) and (STATS121 or STATS111 or ENGEN102).