|20||N/A||N/A||Timetabled hours for the trimester: 60 hours|
(Lecture = 2 hours/week, Laboratory and Problem Based Learning (PBL) tutorial = 3 hours/week)
This is a core unit out of a total of 12 units in the Master of Data Analytics (MDA). This unit addresses the course learning outcomes and complements other units in a related field by developing students’ specialised knowledge of statistical tools and technologies. For further course information refer to: Master of Data Analytics course page. This unit is part of the AQF level 9 (MDA) course.
Students will gain an understanding of AI tools and technologies to learn “hidden” aspects of data, and to interpret this in a useful form. Students will explore machine learning methods and techniques with temporal data, involving different learning mechanisms. Students will have an opportunity to apply their machine learning skills practically onto real-world case studies.
This Unit includes the following topics:
At the completion of this unit students should be able to:
Adopted Reference Style: IEEE
|Assessment Task||Learning Outcomes Assessed*||Assessment Type||Weighting|
|Assignment 1 Individual||a-b||A||15%|
|Assignment 2 Group||c-d||A||15%|
|Laboratory and Problem Based Learning participation & submission||a-e||A||10%|
It is expected that students complete all assessments including the Final Examination. Students must obtain a mark of at least 50% in Task A (assignments, laboratories and PBL tutorials) and 40% in the final examination. In addition students must obtain at least 50 % overall to pass this Unit.
The Final Examination for this Unit is NOT an open book exam. Please note the following examination rules.
Late assignments / exercises:
Late assignments will be penalised at the rate of 10% per day, that is, an assignment is marked out of 90% for 1 day late, 80% for 2 days late, etc., and after 5 working days assignments will attract zero marks.
In the case of serious illness, loss or bereavement, hardship or trauma students may be granted special consideration. A completed Application for Special Consideration and supporting documentation must be submitted online on AMS. This application must be submitted no later than three working days after the due date of the specific piece of assessment or the examination for which the student is seeking Special Consideration. Further information is available at:
MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:
|Ability to communicate||Independent and Lifelong Learning||Ethics||Analytical and Problem Solving||Cultural and Global Awareness||Team work||Specialist knowledge of a field of study|
|Levels of attainment||Extent covered|
|5||The standard is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations|
|4||The standard is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations|
|3||The standard is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations|
|2||The standard is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials|
|1||The standard is not considered, there is no theory or practice or activities associated with this standard|