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This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.
This unit introduces students to the principles and practices of machine learning to uncover patterns and trends in complex data sets, and to visualise these patterns in meaningful ways. Machine learning is a process by which computer models are not explicitly programmed but "learn from data". Students will use existing data to develop models used to predict various outcomes for new data.
Unit is equivalent to MAT3170.
On completion of this unit students should be able to:
Students will attend on campus classes as well as engage in learning activities through ECU's LMS
| Joondalup | Mount Lawley | South West (Bunbury) | |
|---|---|---|---|
| Semester 1 | 13 x 2 hour workshop | Not Offered | Not Offered |
For more information see the Semester Timetable
Students will engage in learning experiences via ECU’s LMS as well as additional ECU learning technologies
GS1 GRADING SCHEMA 1 Used for standard coursework units
Students please note: The marks and grades received by students on assessments may be subject to further moderation. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.
| Type | Description | Value |
|---|---|---|
| Assignment | Data preparation and visualisation | 35% |
| Report | Report on the analysis and modelling of a dataset | 45% |
| Presentation | Video presentation | 20% |
| Type | Description | Value |
|---|---|---|
| Assignment | Data preparation and visualisation | 35% |
| Report | Report on the analysis and modelling of a dataset | 45% |
| Presentation | Video presentation | 20% |
For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.
Students please note: The marks and grades received by students on assessments may be subject to further moderation. Informal vivas may be conducted as part of an assessment task, where staff require further information to confirm the learning outcomes have been met. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.
Integrity is a core value at Edith Cowan University, and it is expected that ECU students complete their assessment tasks honestly and with acknowledgement of other people's work as well as any generative artificial intelligence tools that may have been used. This means that assessment tasks must be completed individually (unless it is an authorised group assessment task) and any sources used must be referenced.
Breaches of academic integrity can include:
Copying the words, ideas or creative works of other people or generative artificial intelligence tools, without referencing in accordance with stated University requirements. Students need to seek approval from the Unit Coordinator within the first week of study if they intend to use some of their previous work in an assessment task (self-plagiarism).
Working with other students and submitting the same or substantially similar work or portions of work when an individual submission was required. This includes students knowingly providing others with copies of their own work to use in the same or similar assessment task(s).
Organising a friend, a family member, another student or an external person or organisation (e.g. through an online website) to complete or substantially edit or refine part or all of an assessment task(s) on their behalf.
Using or having access to unauthorised materials in an exam or test.
Serious outcomes may be imposed if a student is found to have committed one of these breaches, up to and including expulsion from the University for repeated or serious acts.
ECU's policies and more information about academic integrity can be found on the student academic integrity website.
All commencing ECU students are required to complete the Academic Integrity Module.
In some circumstances, Students may apply to their Unit Coordinator to extend the due date of their Assessment Task(s) in accordance with ECU's Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000001386.
Students may apply for Special Consideration in respect of a final unit grade, where their achievement was affected by Exceptional Circumstances as set out in the Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000003318.
MAT3120|3|1
This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.
This unit introduces students to the principles and practices of machine learning to uncover patterns and trends in complex data sets, and to visualise these patterns in meaningful ways. Machine learning is a process by which computer models are not explicitly programmed but "learn from data". Students will use existing data to develop models used to predict various outcomes for new data.
Unit is equivalent to MAT3170.
On completion of this unit students should be able to:
Students will attend on campus classes as well as engage in learning activities through ECU's LMS
| Joondalup | Mount Lawley | South West (Bunbury) | |
|---|---|---|---|
| Semester 1 | 13 x 2 hour workshop | Not Offered | Not Offered |
For more information see the Semester Timetable
Students will engage in learning experiences via ECU’s LMS as well as additional ECU learning technologies
GS1 GRADING SCHEMA 1 Used for standard coursework units
Students please note: The marks and grades received by students on assessments may be subject to further moderation. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.
| Type | Description | Value |
|---|---|---|
| Assignment | Data preparation and visualisation | 35% |
| Report | Report on the analysis and modelling of a dataset | 45% |
| Presentation | Video presentation | 20% |
| Type | Description | Value |
|---|---|---|
| Assignment | Data preparation and visualisation | 35% |
| Report | Report on the analysis and modelling of a dataset | 45% |
| Presentation | Video presentation | 20% |
For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.
Students please note: The marks and grades received by students on assessments may be subject to further moderation. Informal vivas may be conducted as part of an assessment task, where staff require further information to confirm the learning outcomes have been met. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.
Integrity is a core value at Edith Cowan University, and it is expected that ECU students complete their assessment tasks honestly and with acknowledgement of other people's work as well as any generative artificial intelligence tools that may have been used. This means that assessment tasks must be completed individually (unless it is an authorised group assessment task) and any sources used must be referenced.
Breaches of academic integrity can include:
Copying the words, ideas or creative works of other people or generative artificial intelligence tools, without referencing in accordance with stated University requirements. Students need to seek approval from the Unit Coordinator within the first week of study if they intend to use some of their previous work in an assessment task (self-plagiarism).
Working with other students and submitting the same or substantially similar work or portions of work when an individual submission was required. This includes students knowingly providing others with copies of their own work to use in the same or similar assessment task(s).
Organising a friend, a family member, another student or an external person or organisation (e.g. through an online website) to complete or substantially edit or refine part or all of an assessment task(s) on their behalf.
Using or having access to unauthorised materials in an exam or test.
Serious outcomes may be imposed if a student is found to have committed one of these breaches, up to and including expulsion from the University for repeated or serious acts.
ECU's policies and more information about academic integrity can be found on the student academic integrity website.
All commencing ECU students are required to complete the Academic Integrity Module.
In some circumstances, Students may apply to their Unit Coordinator to extend the due date of their Assessment Task(s) in accordance with ECU's Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000001386.
Students may apply for Special Consideration in respect of a final unit grade, where their achievement was affected by Exceptional Circumstances as set out in the Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000003318.
MAT3120|3|2