Applied Artificial Intelligence and Machine Learning

Postgraduate | 2026

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Mode icon
Mode
Mode
Your studies will be on-campus, and may include some online delivery
On campus
area/catalogue icon
Area/Catalogue
ARTI 5002
Course ID icon
Course ID
201716
Campus icon
Campus
Mawson Lakes, Adelaide City Campus East
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course owner
Course owner
School of Comp Sc & IT
Course coordinator
Course coordinator
Georg Grossmann
Course level icon
Course level
1
Work Integrated Learning course
Work Integrated Learning course
No
Study abroad and student exchange icon
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.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes

Course overview

This introductory course aims to equip students with a foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML) through the practical application of algorithms using Python. Students will explore essential pre-deep learning techniques, focusing on implementing and interpreting AI and ML models, and understanding evaluation metrics. The course covers topics such as data preprocessing, supervised learning algorithms like regression and classification, unsupervised learning techniques such as clustering, and a basic introduction to neural networks. Designed to prepare students for advanced studies in deep learning, this course will enable them to apply, assess, and refine ML algorithms effectively, setting a robust foundation for tackling more complex AI challenges in future endeavours.

  • Foundations Of Ai And Supervised Learning
  • Model Evaluation And Advanced Learning Techniques
  • Unsupervised Learning And Neural Networks

Course learning outcomes

  • Comprehensive understanding of AI and ML principles, including basic Python applications
  • Proficiency in data preprocessing techniques (handling missing data, normalization, feature engineering)
  • Ability to critically assess ML models using evaluation metrics and techniques to address overfitting/underfitting
  • Capability to develop, apply, and tune supervised learning algorithms to real-world data
  • Mastery of unsupervised learning methods and their applications to discover patterns in data
  • Basic understanding of neural network architectures and their applications

Availability

Census date Icon
Census date
Fri 27/03/2026
Enrol by date
Enrol by date
Fri 13/03/2026
Last day to W
Last day to W
Fri 10/04/2026
Last day to WF
Last day to WF
Fri 08/05/2026

Class details

Mawson Lakes

Class number
Class number 25916
Section
Section FR01
Size
Size 85
Available
Available 85
Dates Days Time Campus Location Instructor
24 Feb - 14 Apr Tuesday 11:10am - 12pm Mawson Lakes Building F, F1-25
5 May - 2 Jun Tuesday 11:10am - 12pm Mawson Lakes Building F, F1-25
Class number
Class number 25917
Section
Section TU01
Size
Size 20
Available
Available 20
Dates Days Time Campus Location Instructor
24 Feb - 14 Apr Tuesday 12:10pm - 1pm Mawson Lakes Building P, P2-43
5 May - 2 Jun Tuesday 12:10pm - 1pm Mawson Lakes Building P, P2-43
Class number
Class number 25918
Section
Section TU02
Size
Size 20
Available
Available 20
Dates Days Time Campus Location Instructor
24 Feb - 14 Apr Tuesday 1:10pm - 2pm Mawson Lakes Building GP, GP1-06
5 May - 2 Jun Tuesday 1:10pm - 2pm Mawson Lakes Building GP, GP1-06
Class number
Class number 25919
Section
Section TU03
Size
Size 20
Available
Available 20
Dates Days Time Campus Location Instructor
26 Feb - 16 Apr Thursday 1:10pm - 2pm Mawson Lakes Building F, F1-15
7 May - 4 Jun Thursday 1:10pm - 2pm Mawson Lakes Building F, F1-15
Class number
Class number 25920
Section
Section TU04
Size
Size 20
Available
Available 20
Dates Days Time Campus Location Instructor
26 Feb - 16 Apr Thursday 12:10pm - 1pm Mawson Lakes Building F, F1-15
7 May - 4 Jun Thursday 12:10pm - 1pm Mawson Lakes Building F, F1-15
Class number
Class number 29072
Section
Section TU05
Size
Size 20
Available
Available 20
Dates Days Time Campus Location Instructor
26 Feb - 16 Apr Thursday 11:10am - 12pm Mawson Lakes Building F, F1-22
7 May - 4 Jun Thursday 11:10am - 12pm Mawson Lakes Building F, F1-22

Adelaide City Campus East

Class number
Class number 25921
Section
Section FR01
Size
Size 200
Available
Available 200
Dates Days Time Campus Location Instructor
23 Feb - 13 Apr Monday 3:10pm - 4pm Adelaide City Campus East Mawson, G19
4 May - 1 Jun Monday 3:10pm - 4pm Adelaide City Campus East Mawson, G19
Class number
Class number 25922
Section
Section TU01
Size
Size 44
Available
Available 44
Dates Days Time Campus Location Instructor
23 Feb - 13 Apr Monday 1:10pm - 2pm Adelaide City Campus East Ingkarni Wardli, B.15
4 May - 1 Jun Monday 1:10pm - 2pm Adelaide City Campus East Ingkarni Wardli, B.15
Class number
Class number 25923
Section
Section TU02
Size
Size 44
Available
Available 44
Dates Days Time Campus Location Instructor
24 Feb - 14 Apr Tuesday 11:10am - 12pm Adelaide City Campus East Engin and Maths Sciences, G13
5 May - 2 Jun Tuesday 11:10am - 12pm Adelaide City Campus East Engin and Maths Sciences, G13
Class number
Class number 25924
Section
Section TU03
Size
Size 44
Available
Available 44
Dates Days Time Campus Location Instructor
24 Feb - 14 Apr Tuesday 10:10am - 11am Adelaide City Campus East Ingkarni Wardli, B.15
5 May - 2 Jun Tuesday 10:10am - 11am Adelaide City Campus East Ingkarni Wardli, B.15
Class number
Class number 21777
Section
Section TU04
Size
Size 44
Available
Available 44
Dates Days Time Campus Location Instructor
24 Feb - 14 Apr Tuesday 2:10pm - 3pm Adelaide City Campus East Helen Mayo South, S127
5 May - 2 Jun Tuesday 2:10pm - 3pm Adelaide City Campus East Helen Mayo South, S127
Class number
Class number 21778
Section
Section TU05
Size
Size 44
Available
Available 44
Dates Days Time Campus Location Instructor
24 Feb - 14 Apr Tuesday 9:10am - 10am Adelaide City Campus East Ingkarni Wardli, B.15
5 May - 2 Jun Tuesday 9:10am - 10am Adelaide City Campus East Ingkarni Wardli, B.15

Prerequisite(s)

  • must have completed 1 of COMP5002 Problem Solving and Programming Foundations/COMP5004 Programming for Artificial Intelligence and Machine Learning

Corequisite(s)

N/A

Antirequisite(s)

N/A

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