01 Course identification

1. COMP326 · Data Science Foundations
2. COMP327 · Generative AI
3. COMP328 · Machine Learning
4. COMP329 · Advanced ML & Deep Learning
5. COMP429 · AI Systems Engineering
6. COMP529 · Research Topics in AI

02 Course description

This course explores frontier topics and emerging directions in artificial intelligence. Students critically evaluate current research literature, replicate state-of-the-art studies, conduct independent investigations, and develop novel AI solutions. Topics vary according to advances in the field but may include foundation models, reasoning systems, multi-agent intelligence, causal learning, scientific machine learning, neuro-symbolic AI, embodied intelligence, AI safety, and future AI architectures.

The course culminates in an original research project and scholarly report.

03 Prerequisites and assumed knowledge

Assumed capability

You are expected to arrive able to do all of the following without additional teaching. If any of these are unfamiliar, address them in the first two weeks.

04 Learning outcomes

On successful completion of COMP529 you will be able to operate as an independent researcher in artificial intelligence.

05 Teaching and learning approach

COMP529 runs on a research rhythm rather than a lecture rhythm. Each unit follows the same four-part structure, designed to move you from consumer of research to producer of research.

1. Read the literature

   Survey the foundational theory, the seminal papers and the most recent developments in the area.

2. Critique the method

   Interrogate assumptions, evaluation choices and claims. Ask what the paper does not show.

3. Reproduce or extend

   Re-implement a result, or design a small experiment that probes an open question.

4. Discuss and debate

   Bring your findings to the seminar, defend your reasoning and review your peers' work.

Contact modes

06 Course structure

Eight modules, sixteen units and 130 tutorial pages. Each unit surveys a research area: its foundational theory, its seminal papers, its recent developments, and the open challenges that remain.

07 Modules and units

The full unit sequence is listed below. Each unit is supported by a dedicated unit page containing its tutorial sequence, and each tutorial is a single page with research context, foundational theory, seminal papers, recent developments, open challenges, research exercises and discussion questions.

Anatomy of a tutorial

Every tutorial page follows the same structure, so that you always know what to look for and where the research conversation begins.

1. Research contextWhy this topic matters and where it sits in the field.
2. Foundational theoryThe concepts and formalism you need before reading the papers.
3. Seminal papersThe work that defined the area, with guided reading notes.
4. Recent developmentsWhat has changed in the last few years and what is contested.
5. Open challengesThe problems that remain unsolved — candidate research gaps.
6. Research exercisesSmall tasks that require you to do something, not just read.
7. Discussion questionsPrompts brought to the seminar for structured debate.

Example tutorial sequence

The first two units are shown below in full, as an illustration of how a unit's tutorials are ordered. Complete sequences for every unit appear on the unit pages.

08 Assessment

Assessment is overwhelmingly research-based. The five assignments build the skills you need for the major project, which carries the largest weighting.

Weightings are indicative and confirmed in the course LMS.

Assessment tasks

Research assignments

Each assignment rehearses one component of the research process. Together they form a scaffold for the major project; completing them in sequence means that by the time you begin writing the final paper, most of the groundwork is already done.

Submission and marking

09 Major research project

The project runs across the whole semester and carries 40% of your grade. You work through sixteen phases, from topic selection to final presentation, under regular supervision.

Project phases

1. Topic SelectionChoose a research stream and a tractable area within it.
2. Research QuestionFormulate a precise, answerable and worthwhile question.
3. Literature ReviewMap what is known and how it has been established.
4. Gap IdentificationArticulate exactly what is missing or unresolved.
5. Hypothesis FormulationState a testable claim that your experiments can falsify.
6. Method DesignDesign an approach that could genuinely answer the question.
7. Experimental SetupSpecify datasets, metrics, controls and compute budget.
8. Data CollectionGather, document and prepare the data you need.
9. Baseline ModelsEstablish what existing methods already achieve.
10. Proposed MethodImplement and train your approach.
11. EvaluationMeasure performance rigorously against the baselines.
12. Statistical AnalysisTest whether the difference is meaningful, not just present.
13. DiscussionInterpret the results honestly, including negative findings.
14. Future WorkIdentify what you would do next and what remains open.
15. Research PaperWrite up the work to conference standard.
16. PresentationPresent and defend your research at the symposium.

Project milestones

Research tracks

You select one stream for your major research project and pursue it across the second half of the semester, supported by supervision sessions. Each stream is deliberately broad; your contribution will be a specific, tractable question within it.

10 Research symposium

The semester concludes with a mini-conference. You present your work, defend your methodology, answer questions and review the work of your peers.

11 Semester schedule

One unit per teaching week, with assessment milestones running in parallel. The mid-semester break carries no new unit and is intended for project work.

AP = assessment period. Dates are confirmed in the LMS at the start of semester.

12 Resources and infrastructure

Research infrastructure is provided so that your work can be run, checked and reproduced. You are expected to design experiments that fit within realistic resource limits — which is itself a research skill.

13 Academic integrity and generative AI

Research is built on trust. Fabricated results, undisclosed assistance and unattributed text are the most serious offences in this course because they corrupt the record on which everyone else depends.

Use of generative AI tools

Generative AI is a legitimate research tool and you are expected to be competent with it. Its use is permitted, with three conditions.

1. DisclosureRecord which tools you used, for what purpose, and at what stage of the work.
2. VerificationYou are responsible for the correctness of every claim, citation and line of code you submit, whatever produced it.
3. AuthorshipThe intellectual contribution — the question, the method, the analysis and the argument — must be yours.

14 Support and accessibility

15 Reading and research venues

There is no single textbook for COMP529. Reading is drawn from the primary literature, supported by a small number of methodological texts that every researcher should know.

Where to read and where to publish

You are expected to become familiar with the venues that define your chosen stream. At minimum, know the major conferences and journals in your area, their review criteria, and the difference between a workshop paper and a full conference submission.

16 Document control