36120 Advanced Machine Learning Application
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Credit points: 8 cp
Result type: Grade, no marks
Requisite(s): 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.
Description
This subject offers an in-depth exploration of cutting-edge concepts and techniques in machine learning, with a particular focus on the engineering aspects of model deployment. This subject equips students with the necessary knowledge and skills to develop, deploy, and manage machine learning models effectively in real-world applications.
The subject covers advanced machine learning algorithms and methodologies that go beyond the fundamentals. Students gain a deep understanding of the underlying principles behind these techniques and explore their practical applications through hands-on projects and case studies.
A significant emphasis is placed on the engineering aspects of model deployment, ensuring students are well-prepared to handle the challenges associated with integrating machine learning models into production environments.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Implement and evaluate advanced machine learning techniques to real-world problems |
|---|---|
| 2. | Design and implement robust and efficient systems for model deployment |
| 3. | Explain knowledge, skills and best practices required from different disciplines for managing the lifecycle of machine learning models |
| 4. | Identify business outcomes (benefits, risks, cost) and legal and ethical implications (fairness, accountability, privacy and transparency) of deploying machine learning models |
| 5. | Apply effective collaboration and communication skills necessary for working in interdisciplinary teams |
Course intended learning outcomes (CILOs)
This subject contributes specifically to the development of the following course intended learning outcomes:
- Analyse the value of different models, established assumptions and generalisations, about the behaviour of particular systems, for making predictions and informing data discovery investigations (1.3)
- Explore, analyse, manipulate, interpret and visualise data using data science techniques, software and technologies to make sense of data rich environments (2.2)
- Understand and deal critically and openly with the uncertainty, ambiguity and complexity associated with people, systems and data (2.3)
- Apply and assess data science concepts, theories, practices and tools for designing and managing data discovery investigations in professional environments that draw upon diverse data sources, including efforts to shed light on underrepresented components (2.4)
- Critically examine the perceived value of data analytics outcomes and clearly articulate implications for different stakeholders and organisations (3.2)
- Develop a collaborative and team-oriented mindset to harness value for stakeholders to produce innovative solutions to challenges (3.3)
- Explore and craft interpretative narratives that engage key audiences with data analytics and potential significance for action, at a societal, industrial, organisational, group or individual levels (4.2)
- Interrogate and justify ethical responsibilities related to data selection, access, analysis and governance to create a framework for practice (5.2)
Contribution to the development of graduate attributes
Your experiences as a student in this subject support you to develop the following graduate attributes (GA):
- GA 1 Sociotechnical systems thinking
- GA 2 Creative, analytical and rigorous sense making
- GA 3 Create value in problem solving and inquiry
- GA 4 Persuasive and robust communication
- GA 5 Ethical citizenship
Teaching and learning strategies
Blend of online and face to face activities: The subject is offered through a series of teaching sessions which blend online and face-to-face learning. Students learn through interactive lectures and classroom activities making use of the subject materials on canvas. They also engage in individual and collaborative learning activities to understand and apply text analysis techniques in diverse settings.
Authentic problem based learning: This subject offers a range of authentic data science problems to solve that will help develop students’ text analysis skills. They work on real world data analysis problems for broad areas of interest using unstructured data and contemporary techniques.
Collaborative work: Group activities will enable students to leverage peer-learning and demonstrate effective team participation, as well as learning to work in professional teams with an appreciation of diverse perspectives on data science and innovation.
Future-oriented strategies: Students will be exposed to contemporary learning models using speculative thinking, ethical and human-centered approaches as well as reflection. Electronic portfolios will be used to curate, consolidate and provide evidence of learning and development of course outcomes, graduate attributes and professional evolution. Formative feedback will be offered with all assessment activities for successful engagement.
Content (topics)
• Training Advanced Machine Learning Algorithms
• Optimising Machine Learning Models
• Interpreting Machine Learning Results
• Deploying Machine Learning Solutions
• Managing Lifecycle of Machine Learning Models
Assessment
Assessment task 1: Kaggle Competition
| Intent: | The intent of this assessment is to help students gain hands-on experience on a machine learning project |
|---|---|
| Objective(s): | This task addresses the following subject learning objectives: 1, 2, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.3, 2.3, 2.4, 3.2 and 4.2 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 30% |
Assessment task 2: Machine Learning as a service
| Intent: | The intent of this assessment is to help students gain hands-on experience on a real-world project from model training to deployment as a service. |
|---|---|
| Objective(s): | This task addresses the following subject learning objectives: 1, 2, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 2.2, 2.4, 3.2, 4.2 and 5.2 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 30% |
Assessment task 3: Data Product with Machine Learning
| Intent: | The intent of this assessment is to help students gain hands-on experience on building a data product with Machine Learning integration within a team of data scientists. |
|---|---|
| Objective(s): | This task addresses the following subject learning objectives: 1, 2, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 2.2, 2.4, 3.2, 3.3 and 4.2 |
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 40% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.