OL Using Machine Learning Tools PG

Postgraduate | 2026

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Mode
Mode
Your study will be 100% online
100% online
area/catalogue icon
Area/Catalogue
COMP 5091
Course ID icon
Course ID
208480
Campus icon
Campus
Online
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course owner
Course owner
School of Comp Sc & IT
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Course level
1
Work Integrated Learning course
Work Integrated Learning course
No
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No

Course overview

In this course, the students will learn about the fundamentals of machine learning and how to utilise and apply some of the most commonly used tools. The students will learn how to create software that allows the use of pre-existing tool kits when appropriate in order to solve a variety of machine learning problems. The course will have a strong practical component, with case studies and worked examples being used to emphasise the importance of legitimate and verifiable solutions.

Course learning outcomes

  • Adapt industry-standard software tools to model and solve machine learning tasks on real data sets.
  • Evaluate and identify an appropriate method and tool for a given problem and data set.
  • Discriminate between problems and data sets that are amenable to machine learning methods and those that are not.
  • Analyse results and solutions to verify their correctness and identify sources of error.
  • Assess the significance and validity of solutions obtained by multiple methods.
  • Design data management procedures to enable the accurate application of machine learning.

Availability

Census date Icon
Census date
Fri 22/05/2026
Enrol by date
Enrol by date
Fri 01/05/2026
Last day to W
Last day to W
Mon 08/06/2026
Last day to WF
Last day to WF
Mon 22/06/2026

Class details

Online

Class number
Class number 36021
Section
Section 01OL
Size
Size 500
Available
Available 499
Dates Days Time Campus Location Instructor
- Online ,
Notes:
Available to Master of Data Science (Applied) students only.

Prerequisite(s)

  • must have completed all of COMP5058 Foundations of Computer Science - Python B/INFO5045 Applications of Data Science/INFO5046 Real Data: Modern Methods for Finding Hidden Patterns/MATH5022 OL Mathematical Foundations of Data Science

Corequisite(s)

N/A

Antirequisite(s)

  • must not have completed COMP3034 Using Machine Learning Tools OR must not have completed COMPSCI7317OL Using Machine Learning Tools PG at the University of Adelaide

Assumed knowledge

N/A

Textbook(s)

N/A

Inherent requirements

N/A

Other resources

N/A

Assessment

N/A

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The Student Contribution amount displayed below is for students commencing a new program from 2021 onwards. If you are continuing in a program you commenced prior to 1 January 2021, or are commencing an Honours degree relating to an undergraduate degree you commenced prior to 1 January 2021, you may be charged a different Student Contribution amount from the amount displayed below. Please check the Student Contribution bands for continuing students here. If you are an international student, or a domestic student studying in a full fee paying place, and are continuing study that you commenced in 2025 or earlier, your fees will be available here before enrolments open for 2026.