Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor and Lecturer
Dinusha Vatsalan
Lecturer
Mark Dras
|
---|---|
Credit points |
Credit points
10
|
Prerequisites |
Prerequisites
ITEC657 or COMP6200 or COMP8325
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Corequisites |
Corequisites
|
Co-badged status |
Co-badged status
|
Unit description |
Unit description
This unit begins with conventional machine learning techniques for constructing classifiers and regression models, including widely applicable standard techniques such as Naive Bayes, decision trees, logistic regression and support vector machines (SVMs); in this part, given required prior knowledge of machine learning, we focus on more advanced aspects. We then look in detail at deep learning and other state-of-the-art approaches. We discuss in detail the advantages and disadvantages of each method, in terms of computational requirements, ease of use, and performance, and we study the practical application of these methods in a number of use cases. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
From 1 July 2022, Students enrolled in Session based units with written assessments will have the following late penalty applied. Please see https://students.mq.edu.au/study/assessment-exams/assessments for more information.
Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of '0' will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1-hour grace period is provided to students who experience a technical concern.
For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.
Assessments where Late Submissions will be accepted
In this unit, late submissions will be accepted as follows:
Assessment Tasks
Name | Weighting | Hurdle | Due |
---|---|---|---|
Practical Exercises | 30% | No | throughout the semester |
Major Project | 40% | No | week 12 |
Exam | 30% | No | exam period |
Assessment Type 1: Problem set Indicative Time on Task 2: 30 hours Due: throughout the semester Weighting: 30%
These will consist of practical exercises set throughout the semester.
On successful completion you will be able to:
Assessment Type 1: Project Indicative Time on Task 2: 30 hours Due: week 13 Weighting: 40%
The student will apply knowledge of conventional machine learning and deep learning to design and implement a solution to a (classification or other) task on a defined dataset. The deliverables will be the implementation and a report describing this implementation.
On successful completion you will be able to:
Assessment Type 1: Examination Indicative Time on Task 2: 2 hours Due: exam period Weighting: 30%
The examination will require students to understand, apply, analyse and evaluate material drawn from the unit topics.
On successful completion you will be able to:
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Name | Weighting | Hurdle | Due |
---|---|---|---|
Exam | 30% | No | Exam period |
Major Project | 40% | No | Week 12 |
Practical Exercises | 30% | No | throughout the semester |
Assessment Type 1: Examination
Indicative Time on Task 2: 2 hours
Due: Exam period
Weighting: 30%
The examination will require students to understand, apply, analyse and evaluate material drawn from the unit topics.
Assessment Type 1: Project
Indicative Time on Task 2: 30 hours
Due: Week 12
Weighting: 40%
The student will apply knowledge of conventional machine learning and deep learning to design and implement a solution to a (classification or other) task on a defined dataset. The deliverables will be the implementation and a report describing this implementation.
Assessment Type 1: Problem set
Indicative Time on Task 2: 30 hours
Due: throughout the semester
Weighting: 30%
These will consist of practical exercises set throughout the semester.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
We will communicate with you via your university email and through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to the unit convenor via the contact email on iLearn.
For the latest information on the University’s response to COVID-19, please refer to the Coronavirus infection page on the Macquarie website: https://www.mq.edu.au/about/coronavirus-faqs. Remember to check this page regularly in case the information and requirements change during semester. If there are any changes to this unit in relation to COVID, these will be communicated via iLearn..
Week | Topic | Readings (from Géron) |
1 | What is Machine Learning? | Ch 1 |
2 | Workflow of a Machine Learning Project | Ch 2 |
3 | Support Vector Machines and Decision Trees | Ch 3 - 6 |
4 | Ensemble Learning, Random Forests, and Dimensionality Reduction | Ch 7 - 8 |
5 | Handling Text Data | supplementary notes |
6 - 7 | Introduction to Artificial Neural Networks:
|
Ch 10 - 11 |
8 - 9 |
Deep Neural Networks
|
Ch 11 - 14, supplementary notes |
10 |
NNs for sequences, and advanced topics:
|
Ch 15 and onwards, supplementary notes |
11 | Transformers | Supplementary notes |
12 | Machine Learning and security/privacy | Supplementary notes |
13 | Unit review |
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Unit information based on version 2023.01R of the Handbook