This unit introduces students to the foundations and contemporary landscape of Artificial Intelligence. We cover core machine learning concepts, large language models, causal inference, reinforcement learning and computer vision, alongside the philosophical foundations and ethical considerations that underpin the design of trustworthy AI systems.
Mengyan Zhang (Unit Director), Nan Lu, James Cussens
Yuming Li, Siddhant Bansal, Aswathi Thrivikraman
Lecture slides and lab notebooks will be linked here as they become available.
Lecture 1: Mondays, 1200-1300, Ada Lovelace Building SM2
Lecture 2: Tuesdays, 1500-1600, Queens Building 1.58
Lab/Seminar: Wednesdays, 1100-1300, MVB 1.15 (PC Lab)
| Week | Topic | Lecturer | Lecture 1 (Mon, 1200-1300) | Lecture 2 (Tue, 1500-1600) | Lab/Seminar (Wed, 1100-1300) | Reading Materials |
| 1 (w/c 21/09/26) | Introduction | All / Nan Lu | Unit introduction | Introduction to AI | Seminar | |
| 2 (w/c 28/09/26) | Machine Learning - 1 | Mengyan Zhang | Linear Regression and Classification | Neural Networks: Introduction | Lab: Introduction to scikit-learn, Linear Regression. | |
| 3 (w/c 05/10/26) | Machine Learning - 2 | Mengyan Zhang | Neural Networks: CNNs, RNNs, and Transformers | Generative Models | Lab: Introduction to PyTorch, Neural Networks. | |
| 4 (w/c 12/10/26) | Large Language Models | Nan Lu | Large Language Models | Large Language Models | Lab | |
| 5 (w/c 19/10/26) | Philosophy of AI | James Cussens | Philosophy of AI | Philosophy of AI | Seminar | |
| 6 (w/c 26/10/26) | Consolidation week - no scheduled teaching | |||||
| 7 (w/c 02/11/26) | Ethics | Nan Lu | Ethics | Ethics | Seminar | |
| 8 (w/c 09/11/26) | Causal Inference | Mengyan Zhang | Causal Machine Learning | Causal Decision Making | Lab: Causal Machine Learning | |
| 9 (w/c 16/11/26) | Reinforcement Learning | Mengyan Zhang | Multi-Armed Bandits | Reinforcement Learning | Lab: Reinforcement Learning | |
| 10 (w/c 23/11/26) | Computer Vision | Nan Lu / Guest | Computer Vision | Computer Vision (Guest lecture) | Lab | |
| 11 (w/c 30/11/26) | Trustworthy AI | Nan Lu | Trustworthy AI | Trustworthy AI | Lab/Seminar | |
| 12 (w/c 07/12/26) | Revision | No lab | ||||
| 13 (w/c 14/12/26) | Final Exam period | |||||
Students will be assessed via three elements:
The labs are formative exercises which we strongly encourage you to complete, as they are designed to support your understanding of the material covered in lectures.
If you are using UoB machines to do the lab exercises (as opposed to using your own machine) you need to do the following.
module load
anaconda/3-2025
If you want to do the lab exercises on your own machine then you should install Anaconda on it. If you run into installation problems then feel free to ask the Teaching Staff on the unit for help, but we can't guarantee to solve them.
Lab materials and code resources will be linked here.