COMPX310-19A (HAM)

Machine Learning

15 Points

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

Staff

Edit Staff Content

Convenor(s)

Lecturer(s)

Administrator(s)

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.
Edit Staff Content

Paper Description

Edit Paper Description Content
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.
Edit Paper Description Content

Paper Structure

Edit Paper Structure Content
This paper will use Moodle for all communication.
Edit Paper Structure Content

Learning Outcomes

Edit Learning Outcomes Content

Students who successfully complete the course should be able to:

  • .

    - understand current mainstream Machine Learning methods

    - explain the intuition, strengths and weaknesses of current mainstream Machine Learning methods

    - use some current ML toolkits

    - select an appropriate ML algorithm for a given learning problem

    - prepare data, apply selected methods, and present results, for a given ML problem

    Linked to the following assessments:
Edit Learning Outcomes Content
Edit Learning Outcomes Content

Assessment

Edit Assessments Content
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.
Edit Additional Assessment Information Content

Assessment Components

Edit Assessments Content

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
2. Test
11 Apr 2019
4:00 PM
26
  • Hand-in: In Lecture
3. Lab 1
8 Mar 2019
11:30 PM
4
  • Online: Submit through Moodle
4. Lab 2
22 Mar 2019
11:30 PM
4
  • Online: Submit through Moodle
5. Lab 3
5 Apr 2019
11:30 PM
4
  • Online: Submit through Moodle
6. Lab 4
3 May 2019
11:30 PM
4
  • Online: Submit through Moodle
7. Lab 5
17 May 2019
11:30 PM
4
  • Online: Submit through Moodle
8. Lab 6
31 May 2019
11:30 PM
4
  • Online: Submit through Moodle
9. Exam
50
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
Edit Assessments Content

Required and Recommended Readings

Edit Required Readings Content

Recommended Readings

Edit Recommended Readings Content

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (2019)

or

Hands-on Machine Learning with Scikit-Learn andTensorFlow (2017)

and

The Hundred-Page Machine Learning Book (2019)

Edit Recommended Readings Content

Online Support

Edit Online Support Content
Moodle is the main source for online support for this paper.
Edit Online Support Content

Workload

Edit Workload Content
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.
Edit Workload Content

Linkages to Other Papers

Edit Linkages Content

Prerequisite(s)

Prerequisite papers: COMP103 or COMPX101 or ENGG182 or ENGEN103 and STAT121 or STATS121

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: COMP316

Edit Linkages Content