Each lecture day includes a hands-on lab assessment completed during the lab session. Labs are not graded but are a pass/fail requirement — you must complete at least 60% of the lab assessments to pass the module.
| Lab | Date | Topic |
|---|---|---|
| Lab 1 | June 3 | Introduction & Foundational Models |
| Lab 2 | June 10 | Problem Framing, Evaluation & Prompting |
| Lab 3 | June 17 | Context Engineering & Retrieval |
| Lab 4 | June 24 | Multimodal Foundation Models |
| Lab 5 | July 1 | Agentic AI |
| Lab 6 | July 8 | Agentic AI (continued) |
| Lab 7 | July 15 | Fine-tuning & Parameter-Efficient Methods |
| Lab 8 | July 22 | Preference Optimisation & Data Curation |
| Lab 9 | July 29 | Inference Optimisation & Model APIs |
| Lab 10 | August 5 | Backend Systems & Observability |
Labs are pass/fail — they do not contribute to your final grade. However, you must complete a minimum of 6 out of 10 labs (60%) to pass the module. All submissions are via Canvas.
