Availabilities:
| Location | Domestic | International |
|---|---|---|
| Gold Coast | Session 1,3 | Session 1,3 |
Unit description
Introduces students to machine learning. Students will take an algorithmic approach to machine learning in which real-world problems will be solved through machine learning techniques. Students will familiarise themselves with a wide range of algorithms and implement them for problem solving in Python/Octave.
Unit content
Topic 1: Intro to machine learning
Topic 2: Types of machine learning
Topic 3: Regression and prediction
Topic 4: Classification
Topic 5: Neural networks
Topic 6: Machine learning: best practices
Topic 7: Philosophy of machine learning
Learning outcomes
Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.
| On completion of this unit, students should be able to: | GA1 | GA2 | GA3 | GA4 | GA5 | GA6 | GA7 | |
|---|---|---|---|---|---|---|---|---|
| 1 | identify and evaluate the evolution and recent trends in computational intelligence and machine learning | Knowledge of a discipline | Lifelong learning | |||||
| 2 | critically analyse real-world machine learning problems and compare between a range of solutions in a variety of contexts | Creativity | Knowledge of a discipline | Lifelong learning | ||||
| 3 | develop, create and implement machine learning solutions to complicated problems | Creativity | Knowledge of a discipline | |||||
| 4 | plan and implement parts of the tasks in a machine learning pipeline to complete a predictive analysis problem to satisfaction | Creativity | Knowledge of a discipline | |||||
On completion of this unit, students should be able to:
- identify and evaluate the evolution and recent trends in computational intelligence and machine learning
- GA4: Knowledge of a discipline
- GA5: Lifelong learning
- critically analyse real-world machine learning problems and compare between a range of solutions in a variety of contexts
- GA2: Creativity
- GA4: Knowledge of a discipline
- GA5: Lifelong learning
- develop, create and implement machine learning solutions to complicated problems
- GA2: Creativity
- GA4: Knowledge of a discipline
- plan and implement parts of the tasks in a machine learning pipeline to complete a predictive analysis problem to satisfaction
- GA2: Creativity
- GA4: Knowledge of a discipline
Prescribed texts
- Geron, A, 2019, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems , 2nd edn , O'Reilly.
- Prescribed text information is not currently available.
Teaching and assessment
| Teaching method |
| Lecture on-site 1 hour (12 weeks) |
| Tutorial on-site 2 hours (12 weeks) |
| Assessment | |
| Programming assignment | 30% |
| Case study | 30% |
| Exam: oral | 10% |
| Exam: open book | 30% |
Fee information
Domestic
Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.
Commencing 2020 Commonwealth Supported only. Student contribution band: 2
Fee paying courses
For POSTGRADUATE or UNDERGRADUATE full fee paying courses please check Domestic Postgraduate Fees OR Domestic Undergraduate Fees
International
Please check the international course and fee list to determine the relevant fees.