Machine Learning
Duration
- One Semester or Equivalent
Contact hours
- 48 hours
On-campus unit delivery combines face-to-face and digital learning.
2024 teaching periods
| Hawthorn Higher Ed. Semester 1 |
||
|---|---|---|
Dates: Results: Last self enrolment: Census: Last withdraw without fail: |
Aims and objectives
This unit aims to develop and enhance students’ conceptual and practical understanding of machine learning (ML) in the contexts of industry-relevant applications where ML has emerged to play a key role. The students will learn about fundamental concepts, key techniques and popular tools in ML as well as how real-world problems are cast into ML tasks. They will also acquire the ability to apply ML techniques and tools to solve industry-relevant problems.
Unit Learning Outcomes (ULO)
On successful completion of this unit students will be able to:
1 Demonstrate advanced knowledge of fundamental concepts, key techniques and popular tools in machine learning
2 Critically evaluate the roles of machine learning in various real-world applications
3 Critically analyse, evaluate and apply appropriate machine learning techniques and tools to solve various industry-relevant problems
4 Communicate effectively as a professional to technical and non-technical audiences
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
| All Applicable Locations | |||||
|---|---|---|---|---|---|
| Activity Type | Activity | Total Hours | Number of Weeks | Hours Per Week | Optional - Activity Details |
| Face to Face Contact | Lecture | 24 | 12 weeks | 2 | No Description |
| Face to Face Contact | Tutorial Labs | 24 | 12 weeks | 2 | Tutorials in Computer Labs |
| Unspecified Learning Activities | Independent Learning | 102 | 12 weeks | 8.5 | No Description |
| Total Hours: | 150 | Total Hours (per week): | 12.5 | ||
Assessment
| Types | Individual/Group Role | Weighting | Unit Learning Outcomes (ULOs) |
| Assignment | Individual | 30-40% | 1,2 |
| Examination | Individual | 40-50% | 1,2,3 |
| Projet Presentation | Group | 20-30% | 1,2,3,4 |
Content
- Foundations of machine learning, e.g., development history and basic concepts
- Machine learning in real-world applications
- Fundamental machine learning techniques: supervised learning and unsupervised learning
- Advanced machine learning techniques: reinforcement learning, deep learning and transfer learning
- Popular machine learning tools and computing platforms
- Applications of machine learning in various industry-relevant scenarios such as computer vision, natural language processing and Internet of Things
Study resources
- References.
References
A list of reading materials and/or required texts will be made available in the Unit Outline.