Syllabus¶
Last updated: 21 September 2026
Schedule¶
The schedule may be adjusted slightly to match the pace of the class. All sessions, including the exam slot on 21 December, meet at the normal class time and in the normal room. Slides, notebooks, readings, and submission links will be added as they become available.
| Date | Session | Topic | Slides | Lab | Homework |
|---|---|---|---|---|---|
| 7 Sep | S1 | Orientation and foundations | Slides | Survey | Prerequisites |
| 14 Sep | S2 | AI-assisted research workflow | Slides | Notebook · Sol | n/a |
| 21 Sep | S3 | Understand your data | Slides | Notebook | n/a |
| 28 Sep | — | No class — Teachers' Day | n/a | n/a | Notebook |
| 5 Oct | S4 | Regression, loss, and optimisation | |||
| 12 Oct | S5 | Classification: kernels, trees, and boosting | |||
| 19 Oct | S6 | Neural networks | |||
| 26 Oct | — | No class — Retrocession Day | n/a | n/a | |
| 2 Nov | S7 | CNNs and inductive bias | |||
| 9 Nov | S8 | Sequence models and time series | |||
| 16 Nov | S9 | Consolidation and invited seminar | |||
| 23 Nov | S10 | Attention, transformers, and sets | |||
| 30 Nov | S11 | Autoencoders and variational autoencoders | |||
| 7 Dec | S12 | GANs, diffusion, and invited seminar | |||
| 14 Dec | S13 | Final presentations I | n/a | n/a | n/a |
| 21 Dec | Exam slot | Final presentations II | n/a | n/a | n/a |
Assessment¶
| Component | Weight |
|---|---|
| In-class labs and weekly homework | 60% |
| Final project and presentation | 40% |
In-class labs and homework are set as 10–12 assignments across the semester:
- due at noon on Saturday; and
- late submissions are not accepted.
In-class labs count as attendance, so taking part is what is marked — you are not expected to finish every part in the session. Get as far as you can with your group and complete the rest in your own time. Missing a session means no score for that lab, unless you have a good reason such as illness or a family emergency: tell the instructor or a teaching assistant and it will not count against you.
Attendance at the final presentations
The final project presentations on 14 and 21 December are compulsory. Absence without prior permission costs 10 points — 10% of your final grade. If you cannot attend either date, tell the instructor in advance.
Bonus¶
An optional competition will run during the semester, offering extra credit on top of the marks above. Details coming soon.
Use of AI-assisted coding¶
AI-assisted coding is strongly encouraged, but all generated work must be verified. You remain responsible for the correctness of your code, analysis, citations, and conclusions.
AI use must be disclosed in the submitted results.
What a disclosure looks like
A sentence is enough. Say which tool you used and what you used it for:
This website was built with the assistance of Claude Code, for drafting, formatting, and grammar checks.
The training loop was written with GitHub Copilot; I rewrote the loss function myself after the generated version failed a dimensional check.
The first is not hypothetical — it is the disclosure for this course's own site and slides, on the home page.
Tools and accounts¶
- Accounts: complete all required accounts by Session 3. Course services, notebook-computing options, and NSTC Core limits are on the Resources page.
- Software: installation is covered under Software setup.
- Version control: Git is used throughout the course.
Collaboration and submissions¶
- In-class notebook labs are completed in assigned groups of three students. Groups rotate every week; check the teaching assistants' announcement for that week's assignment.
- Submit one set of work per group: a single in-class lab and homework submission covers all three members. Put every member's name on it, with a short note on what each person contributed.
- Accommodations and exceptional circumstances should be discussed with the instructor as early as possible.