| Semester 2, 2022 Online | |
| Units : | 1 |
| Faculty or Section : | Faculty of Health, Engineering and Sciences |
| School or Department : | School of Mathematics, Physics & Computing |
| Grading basis : | Graded |
| Course fee schedule : | /current-students/administration/fees/fee-schedules |
Staffing
Examiner:
Requisites
Pre-requisite: (STA2300 or STA1003 or STA8170) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills
Overview
One of the most common tasks performed by data scientists and data analysts is machine learning for prediction. This introductory course gives an overview of machine learning including concepts, techniques, and algorithms. The course will give students the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered. The course aims at giving a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people for research in data science or practices in data analytics.
Machine learning is the science of getting computer programs to self-improve performance through experiences. In the past decade, machine learning has given us face and speech recognition, recommender systems for music or movies, self-driving cars, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that people probably use it dozens of times a day without knowing it. In this course, students will learn about the most effective machine learning techniques from a variety of perspectives. Students will also gain practice implementing the machine learning techniques and getting them to work for problem solving. More importantly, students will learn about not only the theoretical underpinnings of learning, but also gain the practical know-how to quickly and powerfully apply these techniques to new problems.
Course learning outcomes
On successful completion of this course students should be able to:
- Analyse data using supervised machine learning models and estimate the performance
- Analyse data using unsupervised machine learning models and estimate the performance
- Evaluate the situational requirements of various machine learning applications and justify the appropriate choice of machine learning model
- Given various constraints, optimize a given machine learning model
- Develop a complex model, based on multiple machine learning algorithms, which gives accurate and robust predictions
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. | Machine learning concepts and performance estimation | 10.00 |
| 2. |
Supervised learning (Linear/nonlinear algorithms, decision trees, support vector machines, neural networks) |
35.00 |
| 3. | Unsupervised learning (clustering, dimensionality reduction, deep learning) | 25.00 |
| 4. | Semi-supervised learning | 8.00 |
| 5. | Collaborative filtering and recommendations | 8.00 |
| 6. | Cyber security and other machine learning applications, ethic | 14.00 |
Text and materials required to be purchased or accessed
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(Note: PDF version available from .)
Student workload expectations
To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.
Assessment details
| Description | Group Assessment |
Weighting (%) | Course learning outcomes |
|---|---|---|---|
| Report 1 | No | 20 | 1,3,4 |
| Report 2 | No | 30 | 2,3,4 |
| Report 3 | No | 30 | 1,2,3,4,5 |
| Report 4 | No | 20 | 1,2,3,4,5 |