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 | Jun 03 | Introduction & Foundational Models |
| Lab 2 | Jun 10 | Problem Framing, Evaluation & Prompting |
| Lab 3 | Jun 17 | Retrieval-Augmented Generation |
| Lab 4 | Jun 24 | Multimodal Foundation Models |
| Lab 5 | Jul 01 | Hands on Exercises oon Agentic AI with AMD Technology |
| Lab 6 | Jul 08 | No Lab |
| Lab 7 | Jul 22 | Memory Math, Fine-tuning & Parameter-Efficient Finetuning |
| Lab 8 | Jul 29 | Reinforcement Learning for LLMs |
| Lab 9 | Aug 05 | Inference Optimisation & Model APIs |
| Lab 10 | Aug 12 | Backend Systems & Observability |
Each student will have access to a dedicated lab PC during sessions: Alienware 16X Aurora AC16251 with NVIDIA GeForce RTX 5060 (8 GB GDDR7), Intel Core Ultra 9 275HX (24 cores, up to 5.4 GHz), and 32 GB DDR5 RAM.
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.
