Course coordinator
Dr Yadan Luo
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Selected advanced topics from spatial and multimedia databases: multidimensional data management concepts, theories and technologies, focusing on data access methods and similarity query processing for spatial, multimedia and Web-based databases, with particular emphasis on video indexing and search.
This course provides a comprehensive introduction to multimodal machine learning and high-dimensional data systems, focusing on how modern AI models represent, align, retrieve, and reason over data from multiple modalities such as text, images, and video.
Students will study the evolution of representation learning from early feature encoders to transformer-based architectures, and examine key challenges including the curse of dimensionality, scalability, and efficiency in large-scale multimodal systems. The course covers contrastive learning and vision–language alignment, multimodal large language models (MLLMs), vector databases and approximate nearest neighbour search, retrieval-augmented generation (RAG), and systematic evaluation of generative systems.
Through lectures and case studies, students will gain both theoretical foundations and practical insights into building, optimising, and evaluating modern multimodal AI systems, preparing them for advanced study or industry applications involving large-scale AI pipelines.
Course requirements
Assumed background
Students are assumed to have knowledge covered in courses INFS2200/7903 Relational Database Systems and COMP3506 Algorithm and Data Structures. The following gives the minimum knowledge that students should have in order to take this course (you may refer to INFS2200 Course Profile): good knowledge of SQL, database indexing techniques, DBMS architectures, query processing and optimisation, algorithms and programming.ﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(INFS2200 or INFS7903) and (COMP3506 or COMP7505)
Incompatible
You can't enrol in this course if you've already completed the following:
INFS4200 or INFS7200 or INFS7205
Course contact
Course staff
Lecturer
Dr Yadan Luo
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
This course is designed to help students to learn the latest knowledge, gain insightful understanding and develop skills of critical thinking on large-scale complex data management, for effectively and efficiently managing and searching the information of user interest beyond the traditional relational databases. Through in-depth discussions on spatiotemporal and multimedia data, in this course, we will study the theory and techniques of representing complex data objects as multidimensional feature vectors and processing similarity-based queries. These theories and techniques form the foundation of modern data management, which can also find applications in a wide range of areas such as information retrieval, Web search, bioinformatics, data mining and data science.ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Identify the applications and features of complex data types, including spatial, spatialtemporal and multimedia data.
LO2.
Understand existing advanced spatial and multimedia processing techniques from database management and machine learning perspectives.
LO3.
Examine the major issues of spatial and multimedia data management systems.
LO4.
Analyze and evaluate existing research methods, creative to identify and define problems, and innovative for their solutions for multimedia database management.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Reflection |
Reflective Inquiry (AI-Assisted)
|
25% Five short reflective inquiry submissions across the semester |
(Q1-Q2) Submission 27/03/2026 3:00 pm (Q3-Q5) Submission 8/05/2026 3:00 pm |
| Quiz |
In-Semester Quiz
|
10% |
31/03/2026 5:50 pm |
| Computer Code, Project |
Project
|
20% |
15/05/2026 3:00 pm
Code Interviews will be held in Week 13 |
| Computer Code |
Project (Code Interview)
|
15% |
Week 13 Mon - Week 13 Fri |
| Examination |
Final Exam
|
30% |
End of Semester Exam Period 6/06/2026 - 20/06/2026 |
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
Reflective Inquiry (AI-Assisted)
- Online
- Mode
- Written
- Category
- Reflection
- Weight
- 25% Five short reflective inquiry submissions across the semester
- Due date
(Q1-Q2) Submission 27/03/2026 3:00 pm
(Q3-Q5) Submission 8/05/2026 3:00 pm
- Other conditions
- Longitudinal.
- Learning outcomes
- L01, L02, L03, L04
Task description
Students complete a series of short reflective inquiry tasks supported by AI tools. In each submission, students identify aspects of the course content they do not fully understand, formulate meaningful questions, and use AI as a learning aid to explore these questions.
Marks are awarded based on the quality of students’ reasoning, questioning, and critical reflection on what they have learned. AI-generated output itself is not assessed.
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.
Submission guidelines
This assessment is an individual submission and must be submitted via Blackboard.
Students are required to submit a short written reflective inquiry for each submission window. Each submission should clearly identify aspects of the course content that the student does not fully understand, present meaningful questions, and include critical reflection on what was learned through exploration, which may be supported by AI tools.
The use of AI tools (e.g. large language models) is permitted as a learning aid. However, AI-generated output itself is not assessed. Submissions should be written in the student’s own words and reflect the student’s own understanding, reasoning, and reflection.
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.
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 Quiz
- In-person
- Mode
- Written
- Category
- Quiz
- Weight
- 10%
- Due date
31/03/2026 5:50 pm
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03
Task description
This assessment is an in-semester, closed-book quiz conducted during class time in Week 6. The exam consists of questions designed to assess students’ understanding of core concepts and methods covered in the course.
The exam covers material from Week 2 to Week 5, including multimodal representation, multimodal alignment, and multimodal large language models. Students are expected to demonstrate conceptual understanding and the ability to reason about key ideas introduced in lectures.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Project
- Hurdle
- Mode
- Written
- Category
- Computer Code, Project
- Weight
- 20%
- Due date
15/05/2026 3:00 pm
Code Interviews will be held in Week 13
- Learning outcomes
- L01, L02, L03, L04
Task description
This project is designed to assess your ability to design, implement, and critically evaluate data mining and retrieval techniques in a realistic, open-ended setting.
You are required to build a personalised multimodal chatbot backed by your own knowledge base. The system should retrieve relevant information and answer user queries using multimodal retrieval-augmented generation (RAG) techniques. The emphasis of this project is on data representation, indexing, querying strategies, and evaluation, rather than user interface design or visual polish.
The assessment consists of two compulsory parts:
- Technical Report and Code (20 marks): The report and the code must be submitted as two separate files via Blackboard by 3 pm, Friday of Week 11. Detailed submission requirements will be provided in the project specification, to be released later.
- Code Interview (15): In Part II, you will participate in a code interview scheduled in Week 13. The purpose of this interview is to verify that you understand the code you have written, to confirm that the implementation reflects your own work, and to assess your ability to explain key design decisions and system behaviour.
Important: If a student fails the "Code Interview", the total mark for "Technical Report and Code" will be capped at 5 out of 20, regardless of the quality of the submitted report and code.
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.
Hurdle requirements
If a student fails the "Code Interview", the total mark for "Technical Report and Code" will be capped at 5 out of 20, regardless of the quality of the submitted report and code.Submission guidelines
Please refer to the project specification for details. Please ensure that your code and report are submitted in accordance with the detailed requirements outlined in the project specification. Failure to submit the required files in the correct format may result in a mark of zero.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
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.
Project (Code Interview)
- Hurdle
- Identity Verified
- In-person
- Mode
- Oral
- Category
- Computer Code
- Weight
- 15%
- Due date
Week 13 Mon - Week 13 Fri
Task description
This item forms the secure element of the Project.
You will participate in a code interview scheduled in Week 13. The purpose of this interview is to verify that you understand the code you have written, to confirm that the implementation reflects your own work, and to assess your ability to explain key design decisions and system behaviour.
In accordance with UQ Assessment Policy, your interview will be recorded.”)
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
If a student fails the "Code Interview", the total mark for "Technical Report and Code" will be capped at 5 out of 20, regardless of the quality of the submitted report and code.Submission guidelines
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.
If you are unable to attend your interview in Week 13, one reschedule is permitted. To apply for a reschedule, you need to apply for an extension via my.UQ.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Consistent with industry practice around presentations to clients/industry partners, no late submissions will be accepted and a 100% late penalty applies.
This has been approved by the Associate Dean (Academic)
Final Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
End of Semester Exam Period
6/06/2026 - 20/06/2026
- Other conditions
- Time limited, Secure.
- Learning outcomes
- L01, L02, L03
Task description
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
To pass this course, a minimum of 50% must be obtained in the final exam.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Closed book examination - no written materials 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 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 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
Students must achieve at least 50% (15 out of 30 marks) for the final exam to pass the course. Percentages will not be rounded before any grade cut-offs apply.ﾠThe course coordinator reserves the right to moderate marks.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
As this course is more research-oriented, we provide Applied Classes to further discuss the details. Students also need to focus on paper/online reading to gain the most advanced knowledge.ﾠ
Students will have the opportunity to receive feedback on the first batch of the Reflective Inquiry assessment prior to Census (Week 6) upon request.
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help and 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.
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.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks |
Lecture |
Weekly Lectures Readings/Ref: Lecture Notes; Other Learning outcomes: L01, L02, L03, L04 |
Applied Class |
Applied Class Contacts will be only held between Week 2 - Week 13 Learning outcomes: L01, L02, L03, L04 |
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 for Students Policy and Procedure
- AI for Assessment Guide
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: