COMPX310-22B (TGA)

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)

Bernhard Pfahringer

Bernhard Pfahringer
4041
G.2.23
See Moodle
bernhard.pfahringer@waikato.ac.nz

Nick Lim

Nick Lim
9011
FG.2.07
See Moodle
nick.lim@waikato.ac.nz

Lecturer(s)

Administrator(s)

: buddhika.subasinghe@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 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/

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Paper Structure

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This paper will use Moodle for all communication.
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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • .

    - Demonstrate good understanding of current mainstream Machine Learning methods (test,exam)[WA1,WA7]

    - Explain the intuition, strengths and weaknesses of current mainstream Machine Learning methods (test,exam)[WA1,WA7]

    - Use some current ML toolkits (weekly programming assignments) [WA2,WA3,WA4,WA5,WA9]

    - Select an appropriate ML algorithm for a given learning problem (weekly programming assignments) [WA2,WA3,WA4,WA5,WA9]

    - Prepare data, apply selected methods, and present results, for a given ML problem (weekly programming assignments) [WA2,WA3,WA4,WA5,WA9]

    Linked to the following assessments:
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Assessment

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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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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 50:50. There is no final exam. The final exam makes up 50% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 50:50 or 0:0, whichever is more favourable for the student. The final exam makes up either 50% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Lab 1
22 Jul 2022
11:30 PM
2
  • Online: Submit through Moodle
2. Lab 2
29 Jul 2022
11:30 PM
2
  • Online: Submit through Moodle
3. Lab 3
5 Aug 2022
11:30 PM
2
  • Online: Submit through Moodle
4. Lab 4
12 Aug 2022
11:30 PM
2
  • Online: Submit through Moodle
5. Lab 5
19 Aug 2022
11:30 PM
2
  • Online: Submit through Moodle
6. Lab 6
26 Aug 2022
11:30 PM
2
  • Online: Submit through Moodle
7. Test
12 Sep 2022
5:00 PM
26
  • Hand-in: In Lecture
8. Lab 8
23 Sep 2022
11:30 PM
2
  • Online: Submit through Moodle
9. Lab 9
30 Sep 2022
11:30 PM
2
  • Online: Submit through Moodle
10. Lab 10
7 Oct 2022
11:30 PM
2
  • Online: Submit through Moodle
11. Lab 11
14 Oct 2022
11:30 PM
2
  • Online: Submit through Moodle
12. Lab 12
21 Oct 2022
11:30 PM
4
  • Online: Submit through Moodle
13. Exam
50
✔
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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Recommended Readings

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Online Support

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Moodle is the main source for online support for this paper.
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Workload

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You should expect to spend around 10 hours a week on this paper, including the lecture hours, your lab work, and independent reading and revision.
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Linkages to Other Papers

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Prerequisite(s)

Prerequisite papers: (COMPX101 or ENGEN103) and (STATS121 or STATS111 or ENGEN102).

Corequisite(s)

Equivalent(s)

Restriction(s)

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