COMPX305-20B (TGA)

Practical Data Mining

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)

Lyn Hunt

Lyn Hunt
8338
G.3.21
See Moodle
lyn.hunt@waikato.ac.nz

Lecturer(s)

Maisa Daoud

Maisa Daoud
FG.1.01
See Moodle
maisa.daoud@waikato.ac.nz

Maisa

Maisa
See Moodle
mtdd1@students.waikato.ac.nz

Administrator(s)

Placement/WIL Coordinator(s)

Tutor(s)

: ian.hawthorn@waikato.ac.nz

Student Representative(s)

Lab Technician(s)

Librarian(s)

: 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 is a practical introduction to data mining using machine learning techniques. Students will gain hands-on experience using the Weka open-source machine learning software developed by the Computer Science Department and used by companies and unversities both in New Zealand and overseas.
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Paper Structure

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Labs form a very important part of this paper and it is essential to attend them because they will be marked (apart from the first lab, which is purely introductory). You will have access to the Windows machines in TCBD for the labs.

Lecture attendance is expected. You are responsible for all material covered in class
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Learning Outcomes

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Students who successfully complete the course should be able to:

  • Understand how to apply machine learning methods to solve data mining problems
    Linked to the following assessments:
  • Understand the most widely-used machine learning algorithms
    Linked to the following assessments:
  • Understand the output produced from the algorithms studied
    Linked to the following assessments:
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Assessment

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The internal assessment consists of graded lab submissions, assignments, an in-class test and a final test.
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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. Nine sets of lab exercises of equal weight
27
  • Online: Submit through Moodle
2. Assignment 1
31 Aug 2020
11:30 PM
10
  • Online: Submit through Moodle
3. Assignment 2
16 Oct 2020
3:00 PM
10
  • Online: Submit through Moodle
4. Mid-term test
20 Aug 2020
11:00 AM
20
  • Online: Submit through Moodle
5. Final Test
33
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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I.H. Witten, E. Frank, M.A. Hall, and C.J. Pal. (2016) Data Mining: Practical Machine Learning Tools and Techniques. 4th Ed. Morgan Kaufmann
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Other Resources

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Slides will be available on-line at the course website.
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Online Support

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All information relating to this paper, including your internal assessment marks, will be posted to the Moodle page (elearn.waikato.ac.nz).

All material and lecture recordings will be available online for remote access on Moodle.
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Workload

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There are effectively two one-hour lectures per week, and one two-hour lab, leaving about six hours per week for study and assignment work.
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Linkages to Other Papers

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Prerequisite(s)

Prerequisite papers: (At least one of COMPX101, ENGEN103, COMP103, or ENGG182), (at least one of STATS111, STATS121, STAT111, STAT121), and 30 points at 200 level in Computer Science.

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: COMP321

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