32513 Advanced Data Analytics Algorithms
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Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): (((32130 Fundamentals of Data Analytics OR 31250 Introduction to Data Analytics)) OR ((26776 Foundations of Business Analytics OR 22804 Business Analytics Foundations) AND (42047 Data Processing Using Python OR 43031 Python Programming for Data Processing)) OR 36106 Machine Learning Algorithms and Applications )
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Anti-requisite(s): 31005 Machine Learning
Recommended studies: knowledge of database technologies
Description
Advanced data analytics address the problem of learning from data, which is an exciting field studying 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. This subject takes a machine learning-orientated approach. 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. (C.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 thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
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 4 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.