COMPX521-21A (HAM)

Advanced Machine Learning

15 Points

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Division of Health Engineering Computing & Science
School of Computing and Mathematical Sciences
Department of Computer Science

Staff

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Convenor(s)

Lecturer(s)

Administrator(s)

: rachael.foote@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: alistair.lamb@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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Paper Description

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This paper covers machine learning algorithms such as the ones implemented in the WEKA machine learning workbench, including techniques that deliver state-of-the-art predictive performance.
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Paper Structure

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Slides used in class will be made available on the course webpage, and we will closely follow the text book. However, there will be additional explanations given in class that you may find useful, particularly regarding the assignments, so attending lectures (or viewing them via Moodle) is recommended.
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Learning Outcomes

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Students who successfully complete the course should be able to:

  • understand and implement machine learning algorithms in software environments like WEKA
    Linked to the following assessments:
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Assessment

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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.

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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Assignment 1
16 Apr 2021
5:00 PM
30
2. Assignment 2
4 Jun 2021
5:00 PM
30
3. Test1
16 Apr 2021
1:00 PM
20
4. Test2
4 Jun 2021
1:00 PM
20
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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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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Online Support

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Online support will be provided via Moodle.
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Workload

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Students should expect to spend 10 hours per week on this paper, made up of 2 hours of lectures, and (on average) 8 hours spent working on the assignments and revising lecture notes.

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Linkages to Other Papers

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Prerequisite(s)

Prerequisite papers: (COMPX305 or COMPX310) and COMPX301

Corequisite(s)

Equivalent(s)

Restriction(s)

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