Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.
To check the availability of face-to-face and online activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor, Lecturer
Mark Dras
Contact via via email
4RPD, room 208
by appointment
Lecturer
Rolf Schwitter
Contact via via email
4RPD, room 359
by appointment
Tutor, Lecturer
Fred Amouzgar
by appointment
|
---|---|
Credit points |
Credit points
10
|
Prerequisites |
Prerequisites
ITEC657 or COMP6200
|
Corequisites |
Corequisites
|
Co-badged status |
Co-badged status
COMP7220
|
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:
No extensions will be granted without an approved application for Special Consideration. There will be a deduction of 20% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late. For example, 25 hours late in submission for an assignment worth 10 marks – 40% penalty or 4 marks deducted from the total. No submission will be accepted after solutions have been posted.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Major Project | 40% | No | Initial: end first week of break; final: week 13 |
Practical Exercises | 30% | No | Throughout semester (see iLearn) |
Exam | 30% | No | Exam period |
Assessment Type 1: Project
Indicative Time on Task 2: 30 hours
Due: Initial: end first week of break; final: 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.
Assessment Type 1: Problem set
Indicative Time on Task 2: 30 hours
Due: Throughout semester (see iLearn)
Weighting: 30%
These will consist of practical exercises set throughout the semester.
Assessment Type 1: Examination
Indicative Time on Task 2: 3 hours
Due: Exam period
Weighting: 30%
The examination will require students to understand, apply, analyse and evaluate material drawn from the unit topics.
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
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 | Classical Reinforcement Learning | supplementary notes |
break | ||
7-8 | Introduction to Artificial Neural Networks:
|
Ch 10-11 |
9-10 |
Deep Neural Networks
|
Ch 11-14, supplementary notes |
11-12 |
NNs for sequences, and advanced topics:
|
Ch 15 and onwards, supplementary notes |
13 | Unit review |
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Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to help you improve your marks and take control of your study.
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The topics and assessment are broadly similar to 2020 (which were changed significantly in 2019).
Unit information based on version 2021.02 of the Handbook