Applied Machine Learning
48 hours face to face + blended
One Semester or equivalent
Hawthorn
Available to incoming Study Abroad and Exchange students
Overview
This unit equips students with the essential skills to implement applied machine learning projects. It focuses on techniques and methods used in real world applications, rather than on machine learning theories and statistics.
Requisites
Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Learning outcomes
Students who successfully complete this unit will be able to:
- Explain machine learning life cycle
- Use appropriate data engineering techniques for data preparation
- Analyse and apply advanced machine learning algorithms to solve real-world problems
- Evaluate, deploy and optimise machine learning solutions for given problems
- Interpret and effectively communicate machine learning project outcomes to domain-specific users
Teaching methods
Sarawak
Type | Hours per week | Number of weeks | Total (number of hours) |
---|---|---|---|
Live Online Lecture |
1.00 | 12 weeks | 12 |
Online Lecture |
1.00 | 12 weeks | 12 |
On-Campus Class |
2.00 | 12 weeks | 24 |
Unspecified Activities Independent Learning |
8.5 | 12 weeks | 102 |
TOTAL | 150 |
Assessment
Type | Task | Weighting | ULO's |
---|---|---|---|
Portfolio | Individual | 100% | 1,2,3,4,5 |
Portfolio | Individual | 100% | 1,2,3,4,5 |
Content
- Machine learning life cycle
- Advanced supervised learning
- Semi-supervised and transfer learning
- Deep learning and reinforcement learning
- Data engineering and pre-processing techniques
- Model optimisation
- Model evaluation, deployment and maintenance
- Machine learning project documentation and communication
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.