COMP309 (2019) - Machine Learning Tools and Techniques


This course explores a range of machine learning tools and techniques for analysing data and automatically generating applications. The course will address tools for classification, regression, clustering and text mining, and techniques for preprocessing data and analysing the results of machine learning tools. Students will gain practical experience in applying a range of tools to a range of different data sets from different domains.

Course learning objectives

Students who pass this course will be able to:

  1. Describe a range of standard AI problems, algorithms and tools.
  2. Classify a particular problem into the appropriate category of AI problem.
  3. Choose and apply an appropriate AI algorithm or tool to solve a particular problem, choose appropriate values for the parameters of the algorithm or tool, and be able to evaluate the quality of the solution.
  4. Evaluate the input data for a problem and apply the appropriate tools and techniques to prepare the data for an AI tool.

Withdrawal from Course

Withdrawal dates and process:


Will Browne (Coordinator)

Bing Xue

Teaching Format

The course will be taught by a combination of lectures and tutorials (during the lecture slots).. Laboratories will be introduced if needed to enable students to use the tools and techniques from the lectures and tutorials. The assignments and project will allow students to explore and apply their knowledge to practical data problems, where working at home or in laboratories is permitted.

Student feedback

Student feedback on University courses may be found at:

Dates (trimester, teaching & break dates)

  • Teaching: 08 July 2019 - 13 October 2019
  • Break: 19 August 2019 - 01 September 2019
  • Study period: 14 October 2019 - 17 October 2019
  • Exam period: 18 October 2019 - 09 November 2019

Class Times and Room Numbers

08 July 2019 - 18 August 2019

  • Monday 11:00 - 11:50 – LT323, Hunter, Kelburn
  • Thursday 11:00 - 11:50 – LT104, Hugh Mackenzie, Kelburn
  • Friday 12:00 - 12:50 – LT323, Hunter, Kelburn
02 September 2019 - 13 October 2019

  • Monday 11:00 - 11:50 – LT323, Hunter, Kelburn
  • Thursday 11:00 - 11:50 – LT104, Hugh Mackenzie, Kelburn
  • Friday 12:00 - 12:50 – LT323, Hunter, Kelburn


There are no required texts for this offering.

Mandatory Course Requirements

In addition to achieving an overall pass mark of at least 50%, students must:

  • submit reasonable attempts for at least three of the four assignments, and.
  • submit reasonable attempt at the final project.

If you believe that exceptional circumstances may prevent you from meeting the mandatory course requirements, contact the Course Coordinator for advice as soon as possible.


Assessment ItemDue Date or Test DateCLO(s)Percentage
Assignment 1: Introduction to data mining tools29/07/2019CLO: 1,316%
Assignment 2: Real-World Data Handling, Modelling and Visualisation12/08/2019CLO: 1,2,3,416%
Assignment 3: Kaggle Competition04/09/2019CLO: 2,316%
Assignment 4: Performance Metrics and Optimisation23/09/2019CLO: 1,416%
Project (5 weeks) (Code, scripts, and report on a solution to a problem)28/10/2019CLO: 1,2,3,436%


The penalty for assignments that are handed in late without prior arrangement is one grade reduction per day. Assignments that are more than one week late will not be marked.


Individual extensions will only be granted in exceptional personal circumstances, and should be negotiated with the course coordinator before the deadline whenever possible. Documentation (eg, medical certificate) may be required.

Submission & Return

All work is submitted through the ECS submission system, accessible through the course web pages. Marks and comments will be returned through the ECS marking system, also available through the course web pages.


Although the workload will vary from week to week, you should expect to spend approximately 10–12 hours per week on the course to give a total of 150 hours study time for the course.

Teaching Plan


Communication of Additional Information

All online material for this course can be accessed at

Offering CRN: 30098

Points: 15
Prerequisites: COMP 261 or (DATA 201 and DATA 202) or NWEN 241 or SWEN 221
Duration: 08 July 2019 - 10 November 2019
Starts: Trimester 2
Campus: Kelburn