COMPX305-19B (HAM)

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)

Bernhard Pfahringer

Bernhard Pfahringer
4041
G.1.24
To be advised
bernhard.pfahringer@waikato.ac.nz

Lyn Hunt

Lyn Hunt
8338
G.3.21
To be advised
lyn.hunt@waikato.ac.nz

Lecturer(s)

Lyn Hunt

Lyn Hunt
8338
G.3.21
To be advised
lyn.hunt@waikato.ac.nz

Administrator(s)

: rachael.foote@waikato.ac.nz

Placement Coordinator(s)

Tutor(s)

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 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 Lab 3 and Lab 4, R-block, 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:

  • Outcomes

    Students who successfully complete the course should be able to:

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

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The internal assessment consists of graded lab submissions, assignments, and an in-class test.
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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 67:33. There is no final exam. The final exam makes up 33% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 67:33 or 0:0, whichever is more favourable for the student. The final exam makes up either 33% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Exam
33
✔
2. Nine sets of lab exercises of equal weight
27
3. Two assignments of equal weight
20
  • Online: Submit through Moodle
4. Mid-term in-class test
20
  • Hand-in: In Lecture
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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Moodle
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Workload

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There are 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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