COMPX521-19A (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)

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Placement/WIL Coordinator(s)

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: debby.dada@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 at a more advanced level, 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 so attendance at lectures 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 learning algorithms in environments like WEKA
    Linked to the following assessments:
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Assessment

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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
8 Apr 2019
9:00 AM
30
2. Assignment 2
4 Jun 2019
9:00 AM
30
3. Test1
5 Apr 2019
1:00 PM
20
4. Test2
31 May 2019
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 or COMP316 or COMP321 and a further 30 points at 300 level in Computer Science

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: COMP421, COMP521

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