Session 2 Learning and Teaching Update
The decision has been made to conduct study online for the remainder of Session 2 for all units WITHOUT mandatory on-campus learning activities. Exams for Session 2 will also be online where possible to do so.
This is due to the extension of the lockdown orders and to provide certainty around arrangements for the remainder of Session 2. We hope to return to campus beyond Session 2 as soon as it is safe and appropriate to do so.
Some classes/teaching activities cannot be moved online and must be taught on campus. You should already know if you are in one of these classes/teaching activities and your unit convenor will provide you with more information via iLearn. If you want to confirm, see the list of units with mandatory on-campus classes/teaching activities.
Visit the MQ COVID-19 information page for more detail.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor, Lecturer
Rolf Schwitter
Contact via via email
4RPD, room 359
by appointment
Lecturer
Mark Dras
Contact via via email
4RPD, room 208
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
|
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 (2 hour exam as per new assessment policy) |
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 (2 hour exam as per new assessment policy)
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 |
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 |
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct
Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
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.
The Library provides online and face to face support to help you find and use relevant information resources.
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
For all student enquiries, visit Student Connect at ask.mq.edu.au
If you are a Global MBA student contact globalmba.support@mq.edu.au
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
The new assessment policy launched on 1 July 2021 introduces a limit of 2 hours on the final exam duration (as default).
Unit information based on version 2021.02 of the Handbook