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HE Unit Template
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Applied Machine Learning
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AIWSU7019 - Applied Machine Learning
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20250200
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Course
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1.1
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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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029999
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Information Technology, n.e.c.
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029999 - Information Technology, n.e.c.
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<a href="http://www.federation.edu.au/locate/unit/aiwsu7019">View unit fees</a>
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Graded (e.g. HD, D, C, etc)
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Intermediate
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<p>This subject introduces the foundation and concepts underpinning Machine Learning (ML) at a more abstract level, and provides more focus on its practical applications in areas such as: the classification and extraction of text data from various documents and web pages, image processing, Google's PageRank algorithm and relational data mining (RDM). These learning objectives are achieved through various ML software and a series of practicals and projects. The subject covers the concepts and notions of supervised, unsupervised and reinforcement learning, perceptron, neural networks, support vector machines (SVM), knowledge representation (KR) based RDM, and a comprehensive introduction to the Scikit-learn ML Python libraries.</p>
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<p>• Review of the fundamentals of probability theory, statistics and basic linear algebra notions.</p>
<p>• Installation and introduction to common ML software, which includes the introduction on the use of R and Python as needed for this course.</p>
<p>• Introduction to linear, multiple and logistic regression.</p>
<p>• Model selection, regularization and cross-validation:</p>
<p>• Applications I: Introduction to NLP and classifying text data using logistic regression and naive Bayes.</p>
<p>• Introduction to support vector machines (SVM): Applications II: Classifying text data using SVM classifiers.</p>
<p>• Introduction to neural networks (NN): Applications III: Classifying text data and image data using recurrent and convolutional NN.</p>
<p>• Unsupervised learning: K-Means Clustering and Hierarchical Clustering: Applications IV: Google?fs PageRank algorithm.</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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Semester
0200
Year
Year
2027
Current Year
2026
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AIWSU7019 - Applied Machine Learning
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AIWSU7019 - Applied Machine Learning
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Semester
Semester 1
Semester 2
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Year
2027
2026
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Overview
Key information
Requisites
Learning outcomes
Assessment
Parent Region
Overview
Unit description
<p>This subject introduces the foundation and concepts underpinning Machine Learning (ML) at a more abstract level, and provides more focus on its practical applications in areas such as: the classification and extraction of text data from various documents and web pages, image processing, Google's PageRank algorithm and relational data mining (RDM). These learning objectives are achieved through various ML software and a series of practicals and projects. The subject covers the concepts and notions of supervised, unsupervised and reinforcement learning, perceptron, neural networks, support vector machines (SVM), knowledge representation (KR) based RDM, and a comprehensive introduction to the Scikit-learn ML Python libraries.</p>
Unit content
<p>• Review of the fundamentals of probability theory, statistics and basic linear algebra notions.</p> <p>• Installation and introduction to common ML software, which includes the introduction on the use of R and Python as needed for this course.</p> <p>• Introduction to linear, multiple and logistic regression.</p> <p>• Model selection, regularization and cross-validation:</p> <p>• Applications I: Introduction to NLP and classifying text data using logistic regression and naive Bayes.</p> <p>• Introduction to support vector machines (SVM): Applications II: Classifying text data using SVM classifiers.</p> <p>• Introduction to neural networks (NN): Applications III: Classifying text data and image data using recurrent and convolutional NN.</p> <p>• Unsupervised learning: K-Means Clustering and Hierarchical Clustering: Applications IV: Google?fs PageRank algorithm.</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:
029999 - Information Technology, n.e.c.
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/aiwsu7019">View unit fees</a>
Requisites
Prerequisite
None
Corequisite
None
Exclusion
None
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>Explain the core principles behind machine learning algorithms.</p>
S1
<p>Analyse the scope of current machine learning approaches and applications for both current and future use.</p>
A1
<p>Apply Machine Learning software to real-world problems.</p>
A2
<p>Determine the most appropriate tools to use for machine learning tasks using software applications including Python and R programming languages.</p>
A3
<p>Distinguish between supervised, unsupervised and reinforcement learning notions.</p>
Assessment
Learning Tasks and Assessment
Ref
Learning task
Assessment type
Weighting
Learning outcomes assessed
1
<p>1 hour quiz</p>
<p>Quiz</p>
<p>40%</p>
K1, S1, A2
2
<p>2 hour practical</p>
<p>Practical</p>
<p>20%</p>
A3
3
<p>1,500-word numerical problem solving</p>
<p>Numerical Problem Solving</p>
<p>40%</p>
K1, S1, 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>
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:
029999 - Information Technology, n.e.c.
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/aiwsu7019">View unit fees</a>
Additional information
Cards Section - Non Research
Add Info Static Text
Internal reference: AIWSU7019 - U1.1