320513 Machine Learning
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Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): (230708 Foundation Studio AND 240753 Customer Analytics AND 320146 Data Visualisation and Visual Analytics AND (430031 Python Programming for Data Processing OR 420047 Data Processing Using Python))
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 AND 32513 Advanced Data Analytics Algorithms
Recommended studies: knowledge of database technologies
Description
This subject introduces the essential elements of machine learning - a technique that enables a machine to learn from data and automatically derive or enhance its strategy to perform its tasks. Taking a practical and technical approach, the Machine Learning subject guides learners to the important principles that underlie highly successful machine learning techniques with hands-on experience.
This subject presents learners with core concepts in machine learning as well as a generic framework for machine learning projects. Different learning models, including Decision Trees, Random-Forest, and Neural Networks are discussed and practised with real-world applications dealing with structured (tabular), semi-structured (text) and unstructured data (image).
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Describe the scope, limitations and application of several 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 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. 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)
- Introduction to Machine LearningMachine learning and relationship to statistics and artificial intelligence
- Theory Fundamental ML Models and Techniquesof learning from data
- How to Build Machine Learning Product Important learning models
- Advanced ML Models and Techniques Information theory
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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: | 20% |
Assessment task 2: Proposal on Real-world Data Analysis Project using Machine Learning
| 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: | 35% |
| Length: | The recommended volume of the report is between 1,200 - 2,000 words. Refer to the Canvas site for more details. |
Assessment task 3: Technical understanding of basic machine learning models
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 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: | 45% |
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.
- Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, Springer.
- Chollet, F. (2017). Deep learning with python. Manning Publications.
- Aurlien Gron. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (1st. ed.). O'Reilly Media, Inc.
- Chip Huyen. (2022). Designing Machine Learning Systems. O'Reilly Media, Inc.
- Valliappa Lakshmanan., Valliappa Lakshmanan., Sara Robinson.(2020) Machine Learning Design Patterns. O'Reilly Media, Inc.
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.