| Effective Term: | 2025/05 |
| Institute / School : | Institute of Innovation, Science & Sustainability |
| Unit Title: | Machine Learning |
| Unit ID: | ITECH3500 |
| Credit Points: | 15.00 |
| Prerequisite(s): | (ITECH2500) |
| Co-requisite(s): | Nil |
| Exclusion(s): | (ITECH2111 and ITECH6111 and ITECH7001) |
| ASCED: | 020119 |
| Other Change: | |
| Brief description of the Unit |
This unit provides you with an overview of contemporary trends in artificial learning. You will explore a wide range of topics, including classification, temporal analysis, and predictive analytics, and learn to utilise them to address applications in a variety of domains, such as computer vision and natural language processing. |
| Grade Scheme: | Graded (HD, D, C, P, MF, F, XF) |
| Work Experience Indicator: |
| No work experience |
| Placement Component: No |
| Supplementary Assessment:Yes |
| Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment |
| Course Level: |
| Level of Unit in Course | AQF Level(s) of Course | | 5 | 6 | 7 | 8 | 9 | 10 | | Introductory | | | | | | | | Intermediate | | | | | | | | Advanced | | |  | | | |
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| Learning Outcomes: |
| Knowledge: |
| K1. | Identify and explain a range of deep-learning architectures and methodologies for solving complex problems; |
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| K2. | Recognize complex modelling scenarios such as potential biases in data, and noise and confounding factors that may impact model performance |
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| K3. | Critically evaluate merits and limitations of artificial intelligence architectures, including societal and environmental impacts |
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| Skills: |
| S1. | Design and implement prototypical solutions to complex problems that meet industry guidelines, including strategies to mitigate assessed risks |
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| S2. | Develop methodologies to evaluate and monitor machine learning models, prior to and after deployment |
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| Application of knowledge and skills: |
| A1. | Demonstrate judgement in identifying and adapting machine-learning methodologies to meet requirements |
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| A2. | Effectively communicate machine-learning concepts or proposed solutions |
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| Unit Content: |
Topics may include:
Neural network architectures such as multi-layer perceptrons, convolutional neural networks and recurrent neural networks
Advanced deep-learning architectures that address specific applications such as object detection, temporal predictions
Advanced model training methodologies including loss functions, optimisers and their appropriate use
Validation strategies |
| Graduate Attributes: |
| Federation University recognises that students require key transferable employability skills to prepare them for their future workplace and society. FEDTASKS (Transferable Attributes Skills and Knowledge) provide a targeted focus on five key transferable Attributes, Skills, and Knowledge that are be embedded within curriculum, developed gradually towards successful measures and interlinked with cross-discipline and Co-operative Learning opportunities. One or more FEDTASK, transferable Attributes, Skills or Knowledge must be evident in the specified learning outcomes and assessment for each FedUni Unit, and all must be directly assessed in each Course.
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| FED TASK and descriptor | Development and acquisition of FEDTASKS in the Unit | Learning outcomes
(KSA) | Assessment task
(AT#) | FEDTASK 1
Interpersonal | Students will demonstrate the ability to effectively communicate, inter-act and work with others both individually and in groups. Students will be required to display skills in-person and/or online in: • Using effective verbal and non-verbal communication • Listening for meaning and influencing via active listening • Showing empathy for others • Negotiating and demonstrating conflict resolution skills • Working respectfully in cross-cultural and diverse teams. | Not applicable | Not applicable | FEDTASK 2
Leadership | Students will demonstrate the ability to apply professional skills and behaviours in leading others. Students will be required to display skills in: • Creating a collegial environment • Showing self -awareness and the ability to self-reflect • Inspiring and convincing others • Making informed decisions • Displaying initiative | A1 | AT2 | FEDTASK 3
Critical Thinking and Creativity | Students will demonstrate an ability to work in complexity and ambiguity using the imagination to create new ideas. Students will be required to display skills in: • Reflecting critically • Evaluating ideas, concepts and information • Considering alternative perspectives to refine ideas • Challenging conventional thinking to clarify concepts • Forming creative solutions in problem solving. | S2,A1 | AT2, | FEDTASK 4
Digital Literacy | Students will demonstrate the ability to work fluently across a range of tools, platforms and applications to achieve a range of tasks. Students will be required to display skills in: • Finding, evaluating, managing, curating, organising and sharing digital information • Collating, managing, accessing and using digital data securely • Receiving and responding to messages in a range of digital media • Contributing actively to digital teams and working groups • Participating in and benefiting from digital learning opportunities. | S1, S2, S3 | AT1, AT2 | FEDTASK 5
Sustainable and Ethical Mindset | Students will demonstrate the ability to consider and assess the consequences and impact of ideas and actions in enacting ethical and sustainable decisions. Students will be required to display skills in: • Making informed judgments that consider the impact of devising solutions in global economic environmental and societal contexts • Committing to social responsibility as a professional and a citizen • Evaluating ethical, socially responsible and/or sustainable challenges and generating and articulating responses • Embracing lifelong, life-wide and life-deep learning to be open to diverse others • Implementing required actions to foster sustainability in their professional and personal life. | K3 | AT1 |
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| Learning Task and Assessment: |
| Assessment for this unit will be based on a number of tasks including weekly tasks, written reports, and an end of semester examination covering theoretical aspects of the unit. |
| | Learning Outcomes Assessed | Assessment Tasks | Assessment Type | Weighting | | 1. | K1, K2, K3, S1, S2 | Weekly tasks including: quizzes and exercises | Quizzes and/or exercises | 20% - 40% | | 2. | S1, S2, A1, A2 | Students will conduct research to identify the most appropriate methodology to address a complex problem, including appropriate data management strategies, addressing computational complexity and risks. Students will implement the solution and communicate their findings. | Practical works and accompanying report and/or presentation | 60% - 80% |
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Professional Standards / Competencies: |
| | Standard / Competency | | 1. | Australian Computer Society - Core Body of Knowledge: 2023 accreditation |
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| Attribute | Assessed | Level | | Core ICT Knowledge | |
ICT Fundamentals
| | |
Computational thinking: situation analysis and modelling using a range of methods and patterns to frame it so a computer system could operate effectively within it
| | Yes | Intermediate | | |
Information processing in humans and machines, artificial intelligence
| | Yes | Advanced | | |
History of computing and ICT, drivers of technology evolution and trends for the future
| | Yes | Intermediate | | |
Social and individual impacts of ICT deployment
| | Yes | Intermediate | |
ICT Projects
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Project initiation: stakeholders, benefits specification, scope and requirements, quality and acceptance criteria, cost/benefit, risk and time budgets
| | Yes | Introductory | | Professionalism as it applied in ICT | | Professional ICT Ethics | | |
ICT specific ethics issues: adverse stakeholder impacts of ICT, surveillance and privacy, data matching, autonomous computing, digital divide, etc.
| | Yes | Introductory | |
Impacts of ICT
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Impacts of ICT on society (cyber warfare; surveillance, privacy and civil liberties, cybercrime and hacking, digital divide, technology reliance, intellectual property and legal issues)
| | Yes | Intermediate | | |
Impacts of ICT on organisations, workplaces, jobs and skills
| | Yes | Intermediate |
| | 2. | Skills Framework for the Information Age (SFIA): Version 8 |
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| Attribute | Assessed | Level | | Strategy and architecture | | Strategy and planning | | | RSCH Research (Levels 2 - 6) Systematically creating new knowledge by data gathering, innovation, experimentation, evaluation and dissemination. | | Yes | 3 | | Development and implementation | | Data and analytics | | | DATS Data science (Levels 2 - 7) Applying mathematics, statistics, data mining and predictive modelling techniques to gain insights, predict behaviours and generate value from data. | | Yes | 3 | | | MLNG Machine learning (Levels 2 - 6) Developing systems that learn through experience and by the use of data. | | Yes | 3 | | | BINT Business intelligence (Levels 2 - 5) Developing, producing and delivering regular and one-off management information to provide insights and aid decision-making. | | Yes | 2 |
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