Deep Learning (STAT3007)

Course overview

Deep learning has become a much sought-after game-changing technology that has enabled breakthroughs in applications such as intelligent virtual assistants, medical diagnosis, recommender systems, and autonomous driving.

This course provides a comprehensive and rigorous coverage of deep learning from both applied and theoretical perspectives. Students taking this course will understand how, why and when the algorithms work, and be able to effectively apply deep learning methods to practical problems. This course begins with the basics of machine learning, followed by a broad coverage of deep neural networks, including some major deep neural network architectures, optimization of network parameters, and applications in classification, regression and reinforcement learning.

This course is suitable for both students who want to build data-driven enabling applications with deep learning, and students who want to develop a solid foundation for doing research in deep learning in particular, and machine learning or artificial intelligence more broadly.

To maximise the learning outcomes, students are expected to have a solid foundation in statistics, calculus, linear algebra, and programming. Python will be used for this course.

Deep learning,ﾠa major sub-field of machine learning, has recently been applied to solve manyﾠreal world problems.ﾠThis course gives the students the basic ideas and intuitionﾠbehindﾠdeep learning. Students taking this course willﾠunderstandﾠhow, why and when theﾠalgorithms work, andﾠbe able to effectively apply deep learning methods toﾠpracticalﾠproblems.ﾠThisﾠcourse beginsﾠwith basics of machine learning,ﾠfollowed by a broad coverage on deep neural networks, including some major deep neural network architectures, optimization of network parameters, and applications in classification, regression and reinforcement learning. Python will be used for this course.

In the School of Mathematics and Physics we are committed to creating an inclusive and empowering learning environment for all students. We value and respect the diverse range of experiences our students bring to their education, and we believe that this diversity is crucial for fostering a rich culture of knowledge sharing and meaningful exploration. We hold both students and staff accountable for actively contributing to the establishment of a respectful and supportive learning environment.

Bullying, harassment, and discrimination in any form are strictly against our principles and against UQ Policy, and will not be tolerated. We have developed a suite of resources to assist you in recognising, reporting, and addressing such behaviour. If you have any concerns about your experience in this course, we encourage you to tell a member of the course teaching team, or alternatively contact an SMP Classroom Inclusivity Champion (see Blackboard for contact details). Our Inclusivity Champions are here to listen, to understand your concerns, and to explore potential actions that can be taken to resolve them. Your well-being and a positive learning atmosphere are of utmost importance to us.

Course requirements

Assumed background

Students ﾠenrolled in this course are assumed to have solid foundation in statistics, calculus,ﾠ linear algebra, and programming.ﾠStudents should contact the course coordinator if they are unsure whether or not they have the appropriate background.

Prerequisites

You'll need to complete the following courses before enrolling in this one:

(STAT2004 or STAT2203 or equivalent) + programming experience (for example, MATH2504 or CSSE2002 or equivalent)

Incompatible

You can't enrol in this course if you've already completed the following:

STAT7007 (co-taught)

Course contact

Course staff

Timetable

The timetable for this course is available on the UQ Public Timetable.

Aims and outcomes

Students will learn basic theories, algorithms and models ofﾠdeep learning.ﾠStudents will be required to undertake a project chosen in consultation with the course coordinator, which will lead to a seminar and report. The various assessment tasks aim to develop independence in gaining knowledge and applicable skills, capabilities in using advanced software for machine learning and deep learning, critical thinking and verbal and written communication skills.

Learning outcomes

After successfully completing this course you should be able to:

Assessment

Assessment summary

A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.

Assessment details

Project

• Team or group-based

Mode

Activity/ Performance

Category

Project

Weight

40%

Due date

Proposal 14/04/2025 5:00 pm

Seminar 30/05/2025 5:00 pm

Report 9/06/2025 5:00 pm

Reflective essay 10/06/2025 5:00 pm

The seminars will take place in week 13. The presentation schedule will be released closer to the date.

Learning outcomes

L01, L02, L03, L04, L05

Task description

Submission guidelines

Submit via Blackboard.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

This assessment consists of a sequence of items that must be completed in order, and a maximum extension of 7 days to allow timely completion of the project. See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Assignment 1

Mode

Written

Category

Tutorial/ Problem Set

Weight

20%

Due date

4/04/2025 5:00 pm

Learning outcomes

L01, L02, L03, L04

Task description

Submission guidelines

Submit via Blackboard.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.

See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item.

The maximum extension is 14 days for this assessment to allow timely release of marked reports.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Assignment 2

Mode

Written

Category

Tutorial/ Problem Set

Weight

20%

Due date

16/05/2025 5:00 pm

Learning outcomes

L01, L02, L03, L04

Task description

Submission guidelines

Submit via Blackboard.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.

See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item. The maximum extension is 14 days for this assessment to allow timely release of marked reports.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Final Exam

• Hurdle
• Identity Verified
• In-person

Mode

Written

Category

Examination

Weight

20%

Due date

End of Semester Exam Period

7/06/2025 - 21/06/2025

Learning outcomes

L01, L02, L03, L04

Hurdle requirements

Exam details

Submission guidelines

Deferral or extension

You may be able to defer this exam.

See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Supplementary assessment

Supplementary assessment is available for this course.

Should you fail a course with a grade of 3, you may be eligible for supplementary assessment. Refer to my.UQ for information on supplementary assessment and how to apply. 

Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.  

If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met.  

Supplementary assessment in this course will be a 2-hour examination similar in style to the end-of-semester examination. To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment. 

Additional assessment information

Artificial Intelligence

Assessment tasks in this course evaluate students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.  

Applications for Extensions to Assessment Due Dates

Extension requests are submitted online via my.UQ – applying for an extension. Extension requests received in any other way will not be approved. Additional details associated with extension requests, including acceptable and unacceptable reasons, may be found at my.UQ.

Please note:

• Requests for an extension to an assessment due date must be submitted through your my.UQ portal and you must provide documentation of your circumstances, as soon as it becomes evident that an extension is needed. Your application must be submitted on or before the assessment item's due date and time.
• Applications for extension can take time to be processed so you should continue to work on your assessment item while awaiting a decision. We recommend that you submit any completed work by the due date, and this will be marked if your application is not approved. Should your application be approved, then you will be able to resubmit by the agreed revised due date.
• If an extension is approved, you will be notified via your my.UQ portal and the new date and time for submission provided. It is important that you check the revised date as it may differ from the date that you requested.
• If the basis of the application is a medical condition, applications should be accompanied by a medical certificate dated prior to the assignment due date. If you are unable to provide documentation to support your application by the due date and time you must still submit your application on time and attach a written statement (Word document) outlining why you cannot provide the documentation. You must then upload the documentation to the portal within 24 hours.
• If an extension is being sought on the basis of exceptional circumstances, it must be accompanied by supporting documentation (eg. Statutory declaration).
• For extensions based on a SAP you may be granted a maximum of 7 days (if no earlier maximum date applies). See the Extension or Deferral availability section of each assessment for details. Your SAP is all that is required as documentation to support your application. However, additional extension requests for the assessment item will require the submission of additional supporting documentation e.g., a medical certificate. All extension requests must be received by the assessment due date and time.
• Students may be asked to submit evidence of work completed to date. Lack of adequate progress on your assessment item may result in an extension being denied.
• If you have been ill or unable to attend class for more than 14 days, you are advised to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty - seek advice from the Faculty that administers your program.
• There are no provisions for exemption from an assessment item within UQ rules. If you are unable to submit an assessment piece then, under special circumstances, you may be granted an exemption, but may be required to submit alternative assessment to ensure all learning outcomes are met.

Applications to defer an exam

In certain circumstances you can apply to take a deferred examination for in-semester and end-of-semester exams. You'll need to demonstrate through supporting documentation how unavoidable circumstances prevented you from sitting your exam. If you can’t, you can apply for a one-off discretionary deferred exam.

Deferred Exam requests are submitted online via mySi-net. Requests received in any other way will not be approved. Additional details associated with deferred examinations, including acceptable and unacceptable reasons may be found at my.UQ.

Please note:

• Applications can be submitted no later than 5 calendar days after the date of the original exam.
• There are no provisions to defer a deferred exam. You need to be available to sit your deferred examination.
• Your deferred examination request(s) must have a status of "submitted" in mySI-net to be assessed.
• All applications for deferred in-semester examinations are assessed by the relevant school. Applications for deferred end-of-semester examinations are assessed by the Academic Services Division.
• You’ll receive an email to your student email account when the status of your application is updated.
• If you have a medical condition, mental health condition or disability and require alternative arrangements for your deferred exam you’ll need to complete the online alternative exam arrangements through my.UQ. This is in addition to your deferred examinations request. You need to submit this request on the same day as your request for a deferred exam or supplementary assessment. Contact Student Services if you need assistance completing your alternative exam arrangements request.

All submitted assignments must be your own work. Please readﾠhttps://my.uq.edu.au/information-and-services/manage-my-program/student-integrity-and-conduct/academic-integrity-and-student-conduct.

Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Find the required and recommended resources for this course on the UQ Library website.

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

• Student Code of Conduct Policy
• Student Integrity and Misconduct Policy and Procedure
• Assessment Procedure
• Examinations Procedure
• Reasonable Adjustments - Students Policy and Procedure

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.