COMPX560-21X (HAM)

Turing Topics in Computer Science

15 Points

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

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Convenor(s)

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: rachael.foote@waikato.ac.nz

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Paper Description

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This paper provides an introduction into Deep Learning, focussing on both algorithms and applications.
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Paper Structure

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Viewing of all recorded material is expected, as is participation in the lectures/Q+A sessions. You are responsible for being familiar with all material covered.

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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • Demonstrate good understanding of the basics of Deep Learning (Test1)
    Linked to the following assessments:
  • Demonstrate familiarity with the current mainstream Deep Learning algorithms (Test 2)
    Linked to the following assessments:
  • Build a DL-based image classification from scratch (Assignment + Competition)
    Linked to the following assessments:
  • Employ and adapt pretrained models in DL applications (Assignment + Competition)
    Linked to the following assessments:
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Assessment

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The assessment is based on the four components specified below.
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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. Test 1
8 Apr 2021
6:00 PM
17
  • In Class: In Lecture
2. Programming assignment
4 May 2021
11:30 PM
33
  • Online: Submit through Moodle
3. Kaggle competition
33
  • Online: Submit through Moodle
4. Test 2
8 Jun 2021
4:00 PM
17
  • In Class: 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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There is no required text for this paper.
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Recommended Readings

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There are way too many books on deep learning. Here are my current three favourites:

https://www.deeplearningbook.org

https://d2l.ai

https://github.com/fastai/fastbook

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Other Resources

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Supporting material will be provided through Moodle, as appropriate.
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Online Support

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via Moodle
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Workload

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The expected workload for this paper is around 10 hours for each week during Trimester A, which comprises lecture participation, viewing of recorded material, background reading, and working on the programming assignment and competition.
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Linkages to Other Papers

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This paper builds on prior knowledge coming from both COMPX305 and COMPX310. There may be some overlap with COMPX521, but it will definitely be complimentary to any other Machine Learning related Level 5 offering.
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Prerequisite(s)

Prerequisite papers: 60 points at 300 level in Computer Science. Admission is at the discretion of the Chairperson of Computer Science.

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: COMP460, COMP560

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