Applied Machine Learning

Unit Outline (Higher Education)

Level of Unit in Course

AQF Level(s) of Course

Level of Unit in Course

Introductory

Intermediate

Effective Term:

2025/05

Institute / School :

Institute of Innovation, Science & Sustainability

Unit Title:

Applied Machine Learning

Unit ID:

AIWSU7019

Credit Points:

15.00

Prerequisite(s):

Nil

Co-requisite(s):

Nil

Exclusion(s):

Nil

ASCED:

029999

Other Change:

Brief description of the Unit

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.

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:

Learning Outcomes:

Knowledge:

K1.	Explain the core principles behind machine learning algorithms.

Skills:

S1.	Analyse the scope of current machine learning approaches and applications for both current and future use.

Application of knowledge and skills:

A1.	Apply Machine Learning software to real-world problems.

A2.	Determine the most appropriate tools to use for machine learning tasks using software applications including Python and R programming languages.

A3.	Distinguish between supervised, unsupervised and reinforcement learning notions.

Unit Content:

• Review of the fundamentals of probability theory, statistics and basic linear algebra notions.

• Installation and introduction to common ML software, which includes the introduction on the use of R and Python as needed for this course.

• Introduction to linear, multiple and logistic regression.

• Model selection, regularization and cross-validation:

• Applications I: Introduction to NLP and classifying text data using logistic regression and naive Bayes.

• Introduction to support vector machines (SVM): Applications II: Classifying text data using SVM classifiers.

• Introduction to neural networks (NN): Applications III: Classifying text data and image data using recurrent and convolutional NN.

• Unsupervised learning: K-Means Clustering and Hierarchical Clustering: Applications IV: Google?fs PageRank algorithm.

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.

FED TASK and descriptor		Development and acquisition of FEDTASKS in the Unit
FED TASK and descriptor		Learning outcomes (KSA)	Assessment task (AT#)
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.	Not applicable	Not applicable
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.	Not applicable	Not applicable
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.	Not applicable	Not applicable
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.	Not applicable	Not applicable
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	Not applicable	Not applicable

	Learning Outcomes Assessed	Assessment Tasks	Assessment Type	Weighting
1.	K1, S1, A2	1 hour quiz	Quiz	40%
2.	A3	2 hour practical	Practical	20%
3.	K1, S1, A1	1,500-word numerical problem solving	Numerical Problem Solving	40%

Adopted Reference Style:

APA ()

Professional Standards / Competencies:

Standard / Competency