31005 Machine Learning
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): (31250 Introduction to Data Analytics AND (48024 Programming 2 OR 41091 Data Systems))
Recommended studies: knowledge of database technologies
Description
Machine learning is an exciting field studying of how intelligent agents can learn from and adapt to experience and how to realise such capacity on digital computers. It is applied in many fields of business, industry and science to discover new information and knowledge. At the heart of machine learning are the knowledge discovery algorithms. This subject builds on previous data analytics subjects to give an understanding of how both basic and more powerful algorithms work. It consists of both hands-on practice and fundamental theories. Students learn important techniques in the field by implementation and theoretical analysis. The subject also introduces practical applications of machine learning, especially in the field of artificial intelligence.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Describe the scope, limitations and application of several advanced machine learning methods. (D.1) |
|---|---|
| 2. | Use or program a machine learning method. (D.1) |
| 3. | Design an approach to machine learning problems in specialised domains. (C.1) |
| 4. | Demonstrate an understanding of the issues underlying machine learning to successfully outline an approach to solving a machine learning problem. (D.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
- Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.1)
Contribution to the development of graduate attributes
Engineers Australia Stage 1 Competencies
This subject contributes to the development of the following Engineers Australia Stage 1 Competencies:
- 1.2. Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
- 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.
- 1.4. Discernment of knowledge development and research directions within the engineering discipline.
- 2.1. Application of established engineering methods to complex engineering problem solving.
- 2.2. Fluent application of engineering techniques, tools and resources.
Teaching and learning strategies
The subject is delivered by online learning materials (organised videos of short lectures and algorithm implementation tutorials) and interactive workshops, as well as industry-based guest lectures. The subject features in-depth study of the theory and algorithm of data analytics, as well as detailed hands-on implementation tutorials of classical algorithms. Guest lectures also highlight UTS-specific and industry-based research that give students the opportunity to engage deeply with experts and ask questions that address advanced methods in data analytics. Students will engage with pre-reading material that will be used as basis for discussion and activities in class. Each week an in-class test is made available for students to check their knowledge and gauge their strengths and areas needing further practice. In-class tests with immediate feedback will help students to reflect on their learning. In this subject students have the opportunity to prepare a firm foundation for further study in data science and artificial intelligence by engaging deeply with the project.
Content (topics)
- Machine learning and relationship to statistics and artificial intelligence
- Theory of learning from data
- Important learning models
- Information theory
- Evaluation method and decision making
Assessment
Assessment task 1: Quizzes
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
|---|---|
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 30% |
Assessment task 2: Algorithm Implementation and Journal Reflection
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
|---|---|
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 50% |
| Length: | NA |
Assessment task 3: Presentation and Peer Review
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 and 2 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
|---|---|
| Type: | Presentation |
| Groupwork: | Individual |
| Weight: | 20% |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Recommended texts
You might find the following texts useful.
- Discovering Knowledge in Data, D. T. Larose and C. D. Larose, Wiley, 2014.
- Learning from Data, Y. S. Abu-Mostafa, M. Magdon-Ismail and H-T. Lin, AMLbook.com, 2016.
- Introduction to Data Mining, P.-N. Tan, M. Steinbach and V. Kumar, Addison-Wesley, 2005.
- Machine Learning, Tom M Mitchell, McGraw-Hill, 1997.
- Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2006.
- Data Mining: Concepts and Techniques, J. Han and M. Kamber, Morgan Kaufmann, 2001.
References
The UTS Coursework Assessment Policy & Procedure Manual, at www.gsu.uts.edu.au/policies/assessment-coursework.html.
Other resources
online.uts.edu.au/
Copies of learning materials, assignments and general messages will be available at this web site.