Waikato Paper Outlines

COMPX521-23B (HAM)

Machine Learning Algorithms

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)

Eibe Frank

Eibe Frank

Phone ext: 4396

Room: G.2.18

Office Hours: See Moodle

Email: eibe.frank@waikato.ac.nz

Administrator(s)

Maria Admiraal: maria.admiraal@waikato.ac.nz

Buddhika Subasinghe: buddhika.subasinghe@waikato.ac.nz

Librarian(s)

Anne Ferrier-Watson: anne.ferrier-watson@waikato.ac.nz

You can contact staff by:

Calling +64 7 838 4466 select option 1, then enter the extension.
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 exposes students to selected machine learning algorithms and includes assignments that require the implementation of these algorithms.

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How this paper will be taught

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There will be two 50-minute lectures per week, which will be recorded and made available via Moodle. In the second lecture slot of Week 6 and the second lecture slot of Week 12, there will be in-class tests with short-answer questions. There will also be two assignments. In each of the assignments, the task will be to implement a machine learning algorithm and evaluate it on benchmark datasets. The deliverables will be (a) a report describing the method that has been implemented and analysing the algorithm's performance based on the results obtained in the benchmark experiments and (b) the source code written for the assignment.

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Required Readings

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Witten, I.H., Frank, E., Hall, M., and Pal, C.J. (2016) Data Mining: Practical Machine Learning Tools and Techniques, 4th Edition, Morgan Kaufman.

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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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The understanding of theoretical concepts concerning machine learning algorithms will be assessed in two closed-book, in-class tests. The first test will cover the material discussed in the first half of the lectures. The second test will cover the material from the remaining lectures.

The ability to turn understanding of machine learning algorithms into working code will be assessed in two assignments. In each of the two assignments, a machine learning algorithm from a scientific publication will be chosen by the lecturer to be implemented as individual work by each student. Part of the assignment will be an evaluation of the student's algorithm implementation on benchmark datasets. An assignment report describing the algorithm and the benchmark results obtained, along with the submitted source code, will be used to determine the grade for each assignment.

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.

Component Description	Due Date	Time	Percentage of overall mark
1. Assignment 1	25 Aug 2023	5:00 PM	30
2. Assignment 2	20 Oct 2023	5:00 PM	30
3. Test1	18 Aug 2023	3:00 PM	20
4. Test2	13 Oct 2023	3:00 PM	20

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Section Contents

COMPX521-23B (HAM)

Machine Learning Algorithms

15 Points

Staff

Convenor(s)

Eibe Frank

Lecturer(s)

Administrator(s)

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)