Course Coordinator:Paulo Petersen Saraiva (ppetersen@usc.edu.au) School:School of Science, Technology and Engineering
UniSC Moreton Bay |
Blended learning | Most of your course is on campus but you may be able to do some components of this course online. |
Please go to usc.edu.au for up to date information on the
teaching sessions and campuses where this course is usually offered.
This course gives you a practical introduction to machine learning and deep learning. It introduces you to a variety of learning algorithms and how to use them. You will learn about the key stages of the machine learning process such as algorithm selection, feature selection, model building, diagnostics, cross-validation, and testing.
Activity | Hours | Beginning Week | Frequency |
Blended learning | |||
Learning materials – Pre-recorded concept videos and associated activity | 2hrs | Week 1 | 13 times |
Tutorial/Workshop 1 – On-Campus Computer workshop | 2hrs | Week 1 | 13 times |
300 Level (Graduate)
12 units
Course Learning Outcomes On successful completion of this course, you should be able to... | Graduate Qualities Completing these tasks successfully will contribute to you becoming... | |
1 | Explain common models and processing pipelines in machine learning applications | Knowledgeable |
2 | Apply machine learning algorithms to design solutions for real problems |
Creative and critical thinker Empowered |
3 | Compare benefits/drawbacks of different models and algorithms regarding real use cases |
Knowledgeable Creative and critical thinker Empowered |
4 | Analyse results and solutions to verify their correctness and impact on decision making | Engaged |
5 | Report model selection, implementation, and evaluation in written communication. |
Creative and critical thinker Empowered |
Refer to the UniSC Glossary of terms for definitions of “pre-requisites, co-requisites and anti-requisites”.
CSC201 and MTH212
Not applicable
Not applicable
Not applicable
Standard Grading (GRD)
High Distinction (HD), Distinction (DN), Credit (CR), Pass (PS), Fail (FL). |
Students will complete individual weekly workshop activities under the guidance of the workshop facilitator, providing opportunities for rapid formative feedback throughout the semester.
Delivery mode | Task No. | Assessment Product | Individual or Group | Weighting % | What is the duration / length? | When should I submit? | Where should I submit it? |
All | 1 | Examination - not Centrally Scheduled | Individual | 10% | 2 hours |
Week 6 | Online Assignment Submission with plagiarism check |
All | 2 | Artefact - Technical and Scientific, and Written Piece | Individual | 40% | Code implementation and a brief report to explain the model and results |
Week 11 | Online Assignment Submission with plagiarism check |
All | 3 | Artefact - Technical and Scientific, and Written Piece | Individual | 50% | Code implementation plus 1500 words |
Exam Period | Online Assignment Submission with plagiarism check |
All - Assessment Task 1:Examination | ||||||||||
Goal: | The exam will develop your ability to independently apply your skills and knowledge to solve familiar problem-based questions with confidence within a set time limit. |
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Product: | Examination - not Centrally Scheduled | |||||||||
Format: | This examination consists of a set of questions on the use of machine learning models and algorithms. The questions are based on tutorial activities and course learning materials. |
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Criteria: |
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Generic Skills: | Problem solving, Applying technologies |
All - Assessment Task 2:Data modeling | |||||||||||||
Goal: | Apply machine learning tools to learn models from data sets |
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Product: | Artefact - Technical and Scientific, and Written Piece | ||||||||||||
Format: | You will be presented with a data-related challenge, and will use machine learning tools to learn and evaluate models from the data. |
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Criteria: |
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Generic Skills: | Problem solving, Applying technologies |
All - Assessment Task 3:Machine learning project | ||||||||||||||||
Goal: | You will explore a case study and apply your knowledge of machine learning to design, justify and develop an application to meet the case study requirements. |
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Product: | Artefact - Technical and Scientific, and Written Piece | |||||||||||||||
Format: | 1 software application (code) satisfying the requirements of the case study and 1 report with 1500 words on design decisions justifying the chosen learning models. |
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Criteria: |
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Generic Skills: | Communication, Problem solving, Applying technologies |
A 12-unit course will have total of 150 learning hours which will include directed study hours (including online if required), self-directed learning and completion of assessable tasks. Student workload is calculated at 12.5 learning hours per one unit.
Please note: Course information, including specific information of recommended readings, learning activities, resources, weekly readings, etc. are available on the course Canvas site– Please log in as soon as possible.
Please note that you need to have regular access to the resource(s) listed below. Resources may be required or recommended.
Required? | Author | Year | Title | Edition | Publisher |
Required | Aurélien Géron | 2019 | Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow | 2 | O'Reilly |
Not applicable
Academic integrity is the ethical standard of university participation. It ensures that students graduate as a result of proving they are competent in their discipline. This is integral in maintaining the value of academic qualifications. Each industry has expectations and standards of the skills and knowledge within that discipline and these are reflected in assessment.
Academic integrity means that you do not engage in any activity that is considered to be academic fraud; including plagiarism, collusion or outsourcing any part of any assessment item to any other person. You are expected to be honest and ethical by completing all work yourself and indicating in your work which ideas and information were developed by you and which were taken from others. You cannot provide your assessment work to others. You are also expected to provide evidence of wide and critical reading, usually by using appropriate academic references.
In order to minimise incidents of academic fraud, this course may require that some of its assessment tasks, when submitted to Canvas, are electronically checked through Turnitin. This software allows for text comparisons to be made between your submitted assessment item and all other work to which Turnitin has access.
Eligibility for Supplementary Assessment Your eligibility for supplementary assessment in a course is dependent of the following conditions applying: The final mark is in the percentage range 47% to 49.4% The course is graded using the Standard Grading scale You have not failed an assessment task in the course due to academic misconduct
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