| Effective Term: | 2027/05 |
| Institute / School : | Institute of Innovation, Science & Sustainability |
| Unit Title: | Artificial Intelligence and Machine Learning |
| Unit ID: | ITECH7001 |
| Credit Points: | 15.00 |
| Prerequisite(s): | (ITECH5104) |
| Co-requisite(s): | (An approved mathematics or information technology elective.) |
| Exclusion(s): | (ITECH2111) |
| ASCED: | 020119 |
| Other Change: | |
| Brief description of the Unit |
Artificial intelligence and machine learning are increasingly important in the rapidly advancing technological landscape. They play a role in many aspects of life. While the scope of applications is diverse and useful, they also come with a host of philosophical and ethical considerations. This unit exposes students to the theory and practical methods associated with the field of artificial intelligence (AI). Students will gain an appreciation for the philosophy, history and applications of artificial intelligence. They will gain an understanding of the functioning of core algorithms within AI, and skills in the application of software tools which implement those algorithms. Areas covered will include knowledge representation, logic and automated reasoning, search, and modelling uncertainty, with a particular emphasis on techniques associated with various areas of machine learning, including unsupervised, supervised and reinforcement learning. Students will also be required to consider the ethics associated with the development and deployment of AI technology within society, and understand the importance of factors such as fairness, safety and explainability. |
| Grade Scheme: | Graded (HD, D, C, P, MF, F, XF) |
| Work Experience Indicator: |
| No work experience |
| Placement Component: | |
| 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 historical, current, and emerging artificial intelligence algorithms and methodologies for solving complex problems. |
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| K2. | Explain how to design and deploy artificial intelligence so as to produce beneficial and equitable outcomes for society. |
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| Skills: |
| S1. | Represent knowledge using different techniques to solve complex problems; |
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| S2. | Select, configure, adapt, and apply appropriate algorithmic approaches for solving a variety of complex problems in real-world situations. |
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| S3. | Prepare data for use as input to machine learning systems. |
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| S4. | Interpret, compare and report on algorithm output and performance in different contexts. |
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| Application of knowledge and skills: |
| A1. | Research, interpret, and communicate appropriate developments in Artificial Intelligence. |
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| Other outcomes: |
| Unit Content: |
Topics may include:
1. History and philosophy behind artificial intelligence; current and future applications of artificial intelligence; social implications of AI
2. Logic and search;
3. Knowledge representation, and reasoning - including reasoning with uncertainty;
4. Machine learning - overview, development processes and tools;
5. Supervised and semi-supervised learning;
6. Dimension reduction, clustering and unsupervised learning;
7. Neural networks and deep learning; deep learning architectures; and
8. Reinforcement learning. |
| 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 | | Level | FEDTASK 1
Interpersonal | Students at this level will demonstrate an advanced ability in a range of contexts to effectively communicate, interact and work with others both individually and in groups. Students will be required to display high level skills in-person and/or online in: • Using and demonstrating a high level of verbal and non-verbal communication • Demonstrating a mastery of listening for meaning and influencing via active listening • Demonstrating and showing empathy for others • High order skills in negotiating and conflict resolution skills\\ • Demonstrating mastery of working respectfully in cross-cultural and diverse teams. | | FEDTASK 2
Leadership | Students at this level will demonstrate a mastery in professional skills and behaviours in leading others. • Creating and sustaining a collegial environment • Demonstrating a high level of self -awareness and the ability to self-reflect and justify decisions • Inspiring and initiating opportunities to lead others • Making informed professional decisions • Demonstrating initiative in new professional situations. | | FEDTASK 3
Critical Thinking and Creativity | Students at this level will demonstrate high level skills in working in complexity and ambiguity using the imagination to create new ideas. Students will be required to display skills in: • Reflecting critically to generate and consider complex ideas and concepts at an abstract level • Analysing complex and abstract ideas, concepts and information • Communicate alternative perspectives to justify complex ideas • Demonstrate a mastery of challenging conventional thinking to clarify complex concepts • Forming creative solutions in problem solving to new situations for further learning. | Y - Present | FEDTASK 4
Digital Literacy | Students at this level will demonstrate the ability to work competently across a wide range of tools, platforms and applications to achieve a range of tasks. Students will be required to display skills in: • Mastering, exploring, evaluating, managing, curating, organising and sharing digital information professionally • Collating, managing complex data, accessing and using digital data securely • Receiving and responding professionally to messages in a range of professional digital media • Contributing competently and professionally to digital teams and working groups • Participating at a high level in digital learning opportunities. | Y - Present | FEDTASK 5
sustainable and Ethical Mindset | Students at this level will demonstrate a mastery of considering and assessing the consequences and impact of ideas and actions in enacting professional ethical and sustainable decisions. Students will be required to display skills in: • Demonstrate informed judgment making that considers the impact of devising complex solutions in ambiguous global economic environmental and societal contexts • Professionally committing to the promulgation of social responsibility • Demonstrate the ability to evaluate ethical, socially responsible and/or sustainable challenges and generating and articulating responses • Communicating lifelong, life-wide and life-deep learning to be open to the diverse professional others • Generating, leading and implementing required actions to foster sustainability in their professional and personal life | Y - Present |
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| | Learning Outcomes Assessed | Assessment Tasks | Assessment Type | Weighting | Professional Standards |
| 1. |
K1, S1, S2, S3 |
Complete weekly tasks to build a portfolio of learning on how to solve problems using Artificial Intelligence and Machine Learning techniques. |
Lab tasks and portfolio/journal |
20% - 30% |
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| 2. |
K2, S1, S2, S3, S4, A1 |
Apply Artificial Intelligence and Machine Learning techniques to a real or simulated industry dataset, and analyse their performance using standard evaluation techniques. Prepare a report describing methodology, evaluation, and results, including effect of hyperparameters and any ethical considerations in implementation. |
Written Report |
40% - 70% |
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| 3. |
K2, S1, S3, S4, A1 |
Answer questions covering how Artificial Intelligence and Machine Learning algorithms and methodologies can be applied to hypothetical problem scenarios. |
Written or Oral Test(s) |
10% - 30% |
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