Course coordinator
Professor Feng Liu
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
An advanced course designed to deepen student knowledge and capability in computational techniques in areas of particular importance to engineering. Topics are drawn from linear algebra, stochastic systems and optimisation theory with emphasis on applications and examples in various fields of engineering including but not limited to biomedical engineering, electricity market, embedded systems and microwave and telecommunications. Practical skills in MATLAB programming are developed.
The course aims to give students the computational tools and theory needed for postgraduate-level study of engineering. Topics to be covered in the course will be drawn from the following:
I. Matrix Algebra
Computation of matrix and vector norms, inverses, Gaussian elimination, pivoting, sensitivity, condition number, generalised inverses (Moore-Penrose), idempotent matrices, solution of matrix equations, projection matrices, determinants, cofactors, banded, circulant, Vandermonde & Toeplitz matrices, definiteness, Givens rotations, computation of decompositions (Cholesky, LQ/QR, singular value, eigenvalues/eigenvectors), matrix differentiation.
II. Stochastic Processes
Different types of stochastic processes: Bernoulli processes, Poisson processes, and Markov chain. Bayesian inference: Maximum A Posteriori estimation, Least Mean Square estimation, and Kalman filter. Decision making under uncertainty: Markov Decision Processes and Partially Observable Markov Decision Processes.
III. Optimisation
Linear programming (simplex algorithm, Karmakar's method), unconstrained optimisation (gradient descent, Newton and quasi-Newton's method, conjugate gradient, Levenberg-Marguardt), convex optimisation (Lagrange multipliers, KKT conditions), stochastic and heuristic optimization (random search, simulated annealing, evolutionary algorithms, metaheuristics).
IV. Applications in Engineering
MATLAB as an engineering tool, applications to telecommunications, biomedical engineering, embedded systems, electricity market, robotics, and other fields of engineering as appropriate.
Assessment dates and materials are tentative, depending on how fast the materials can be covered in class. Please follow the class and announcements in blackboard for the most updated dates.
Course requirements
Assumed background
A basic undergraduate-level background in engineering mathematics and computational techniques is assumed. In particular, it is assumed that the student has a basic understanding of linear algebra, probability and statistics and optimisation, on which the more advanced material in this course will build.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
MATH2001 and MATH2010 and (STAT2201 or STAT2202)
Restrictions
Engineering Postgraduate suite.
Course contact
Course staff
Lecturer
Professor Feng Liu
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The course aims to give students the computational tools and theory needed for postgraduate-level study of engineering. Specifically, the aim is to ensure that the student has excellent skills in MATLAB for numerical implementation of advanced methods in optimisation, stochastic processes and linear algebra.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Analyse electrical engineering problems to determine where computational techniques in matrix algebra, stochastic systems and optimisation theory can be applied.
LO2.
Interpret the theoretical foundations of matrix algebra, stochastic systems and optimisation theory
LO3.
Design solutions for electrical engineering problems by applying key algorithms in matrix algebra, stochastic systems and optimisation theory
LO4.
Implement computational techniques in MATLAB with a high level of proficiency
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation |
Assignment 1
|
10% |
23/08/2024 3:00 pm |
| Examination |
In semester exam
|
28% |
17/09/2024 6:00 pm
The exam will take place outside of scheduled class time from 6:00 pm to 8:00 pm on 17/09/24. |
| Computer Code, Paper/ Report/ Annotation, Tutorial/ Problem Set | Assignment 2 | 34% |
23/10/2024 3:00 pm |
| Examination |
Final Exam
|
28% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
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
Assignment 1
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 10%
- Due date
23/08/2024 3:00 pm
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L03, L04
Task description
The assignments will be designed to develop skills in problem solving based on the theory presented in lectures on the first part (optimisation), often using MATLAB.
Submission guidelines
Submit the assignment (report and code) via the Blackboard turnitin assignment submission system.
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.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14 days where the earlier time frame applies if no extensions.
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.
In semester exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 28%
- Due date
17/09/2024 6:00 pm
The exam will take place outside of scheduled class time from 6:00 pm to 8:00 pm on 17/09/24.
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03
Task description
This exam will take place outside of scheduled class time (6:00-8:00pm, 17/09/24) and will cover the Optimisation part.
The test will be 90 minutes in duration. This test will be closed-book and may contain multiple-choice, short-answer, and problem-solving questions.
Multiple choice assessment is not used for over 50% of the test.
Hurdle requirements
To achieve a Grade 4 (Pass) or higher in the course, students need to score at least an average of 40% on both the in-semester exam and the final exam.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - specified written materials permitted |
| Materials | One A4 sheet of handwritten notes, double sided, is permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Assignment 2
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation, Tutorial/ Problem Set
- Weight
- 34%
- Due date
23/10/2024 3:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
There are three parts in this assignment.
In the first part, the assignment questions will be designed to develop skills in problem-solving based on the theory presented in lectures on Optimisation, often using MATLAB.
In the second part, the assignment questions will be designed to develop skills in problem-solving based on the theory presented in lectures on Linear Algebra, often using MATLAB.
In the third part, students must prepare a report and presentation. Presentation details will be provided in the specification of Assignment 2.
Submission guidelines
Submit the assignment (report and code) via the Blackboard turnitin assignment submission system.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14 days where the earlier time frame applies if no extensions.
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.
Marked submission with feedback and/or detailed solutions will be released to students within 14-21 days where the earlier time frame applies if no extensions.
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
- 28%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03
Task description
The final exam covers linear algebra and stochastic processes parts.
Hurdle requirements
To achieve a Grade 4 (Pass) or higher in the course, students need to score at least an average of 40% on both the in-semester exam and the final exam.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - specified written materials permitted |
| Materials | One A4 sheet of handwritten notes, double sided, is permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 20 - 44 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Marks are rounded to the nearest integer before calculating the final grade. Half-integers are rounded up. At the discretion of the coordinator, the final marks of the class may be scaled upwards but not downwards.ﾠ
To achieve a Grade 4 (Pass) or higher in the course, students need to score at least an average of 40% on both the in-semester exam and the final exam.
ﾠ
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Having Troubles?
If you are having difficulties with any aspect of the course material you should seek help. Speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Complex / authentic assessment using AI and/or MT to support learning
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of 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.
Additional learning resources information
- Lectures
- Readings that will be provided over the duration of the course
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 6 |
Lecture |
Optimisation Learning outcomes: L01, L02, L03 |
Week 1 (22 Jul - 28 Jul) |
Lecture |
Introduction + Introduction to Matlab Learning outcomes: L04 |
Multiple weeks From Week 2 To Week 13 |
Tutorial |
General contacts This course will offer a weekly 2-hour tutorial session from week 2 to week 13. Learning outcomes: L01, L02, L03, L04 |
Multiple weeks From Week 7 To Week 11 |
Lecture |
Linear Algebra Learning outcomes: L01, L02, L03, L04 |
Multiple weeks From Week 12 To Week 13 |
Lecture |
Stochastic Processes Learning outcomes: L01, L02, L03 |
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.
School guidelines
Your school has additional guidelines you'll need to follow for this course: