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Artificial Intelligence and Machine Learning
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ITECH7001 - Artificial Intelligence and Machine Learning
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20270200
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Course
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3.1
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2026-09-10
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2026-09-10
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Information Technology
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Institute of Innovation, Science & Sustainability
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Information Technology
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Postgraduate - Coursework
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15
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.125
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020119
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Artificial Intelligence
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020119 - Artificial Intelligence
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<a href="http://www.federation.edu.au/locate/unit/itech7001">View unit fees</a>
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Graded (e.g. HD, D, C, etc)
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Masters Degree
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Introductory
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<p>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.</p>
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<p>Topics may include:</p>
<p>1. History and philosophy behind artificial intelligence; current and future applications of artificial intelligence; social implications of AI</p>
<p>2. Logic and search;</p>
<p>3. Knowledge representation, and reasoning - including reasoning with uncertainty;</p>
<p>4. Machine learning - overview, development processes and tools;</p>
<p>5. Supervised and semi-supervised learning;</p>
<p>6. Dimension reduction, clustering and unsupervised learning;</p>
<p>7. Neural networks and deep learning; deep learning architectures; and</p>
<p>8. Reinforcement learning.</p>
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<p>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.</p>
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2027
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2026
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ITECH7001 - Artificial Intelligence and Machine Learning
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Overview
Key information
Requisites
Learning outcomes
Assessment
FedTASK
Parent Region
Overview
Unit description
<p>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.</p>
Unit content
<p>Topics may include:</p> <p>1. History and philosophy behind artificial intelligence; current and future applications of artificial intelligence; social implications of AI</p> <p>2. Logic and search;</p> <p>3. Knowledge representation, and reasoning - including reasoning with uncertainty;</p> <p>4. Machine learning - overview, development processes and tools;</p> <p>5. Supervised and semi-supervised learning;</p> <p>6. Dimension reduction, clustering and unsupervised learning;</p> <p>7. Neural networks and deep learning; deep learning architectures; and</p> <p>8. Reinforcement learning.</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Information Technology
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
020119 - Artificial Intelligence
Offering:
<p><strong>Semester 1</strong><p>Ballarat - Mt Helen (On Campus)</p><p>Melbourne City Campus (On Campus)</p><p>On-line Learning</p></p>
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/itech7001">View unit fees</a>
Requisites
Prerequisite
<p>must have completed <a href="/ords/r/curriculum/catalogue/course?p6_code=ITECH5104" target="_blank">ITECH5104</a></p>
Corequisite
None
Exclusion
<p>must not have completed <a href="/ords/r/curriculum/catalogue/course?p6_code=ITECH2111" target="_blank">ITECH2111</a>/<a href="/ords/r/curriculum/catalogue/course?p6_code=ITECH2500" target="_blank">ITECH2500</a>/<a href="/ords/r/curriculum/catalogue/course?p6_code=ITECH3500" target="_blank">ITECH3500</a></p>
Learning outcomes
Learning outcomes
<p>On successful completion of this unit, students are expected to gain the following [K]nowledge, [S]kills and [A]pplication of knowledge & skills</p>
Learning Outcomes
Ref
Outcome
K1
<p>Identify and explain a range of historical, current, and emerging artificial intelligence algorithms and methodologies for solving complex problems.</p>
K2
<p>Explain how to design and deploy artificial intelligence so as to produce beneficial and equitable outcomes for society.</p>
S1
<p>Represent knowledge using different techniques to solve complex problems;</p>
S2
<p>Select, configure, adapt, and apply appropriate algorithmic approaches for solving a variety of complex problems in real-world situations.</p>
S3
<p>Prepare data for use as input to machine learning systems.</p>
S4
<p>Interpret, compare and report on algorithm output and performance in different contexts.</p>
A1
<p>Research, interpret, and communicate appropriate developments in Artificial Intelligence.</p>
Assessment
Learning Tasks and Assessment
Ref
Learning task
Assessment type
Weighting
Learning outcomes assessed
1
<p>Complete weekly tasks to build a portfolio of learning on how to solve problems using Artificial Intelligence and Machine Learning techniques.</p>
<p>Lab tasks and portfolio/journal</p>
<p>20% - 30%</p>
K1, S1, S2, S3
2
<p>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.</p>
<p>Written Report</p>
<p>40% - 70%</p>
K2, S1, S2, S3, S4, A1
3
<p>Answer questions covering how Artificial Intelligence and Machine Learning algorithms and methodologies can be applied to hypothetical problem scenarios.</p>
<p>Written or Oral Test(s)</p>
<p>10% - 30%</p>
K2, S1, S3, S4, A1
Supplementary assessment available:
Yes
Supplementary assessment information:
<p>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.</p>
FedTASK
FedTASK
<p>Federation University recognises that students require key transferable employability skills to prepare them for their future workplace and society. FedTASKs (<strong>T</strong>ransferable <strong>A</strong>ttributes <strong>S</strong>kills and <strong>K</strong>nowledge) provide a targeted focus on five key, transferable Attributes, Skills, and Knowledge that are embedded within curriculum, developed gradually towards successful measures and interlinked with cross-discipline and co-operative learning opportunities.</p>
FedTASK
Description of FedTASK outcome
FedTASK 1: Interpersonal
FedTASK 2: Leadership
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
FedTASK4: 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
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
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Information Technology
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
020119 - Artificial Intelligence
Offering:
<p><strong>Semester 1</strong><p>Ballarat - Mt Helen (On Campus)</p><p>Melbourne City Campus (On Campus)</p><p>On-line Learning</p></p>
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/itech7001">View unit fees</a>
Additional information
Add Info Static Text
Internal reference: ITECH7001 - U3.1