This unit introduces students to a range of methodologies and applications across the field of AI. Each week will focus on a different theme, with three activities:
Required reading -- background reading to introduce the topic of the week
Guest seminars (TB2: Mondays, 1300-1400, Queen's 1.58) -- a guest speaker will present an introduction to their research area, connected to the week's topic
Group discussion (TB2: Fridays, 1000-1100, Queen's 1.68) -- student led discussion on the required reading and guest seminars. This is a chance to go over the important concepts and raise questions. The sessions will be chaired by a student, to facilitate the discussion (they are not required to present the topic).
Link to spreadsheet with session chairs
AI Lab Lunch and Learn Seminars: (usually Wednesdays at 1330-1500, Queen's 1.07) -- these sessions are not exclusive to the CDT and we invite the whole AI Lab and beyond, with visiting researchers as well as our local speakers giving talks, and, importantly, free pizza. Many foundational and applied AI topics are covered here, so it is worth attending.
Bear with us while we organise the schedule below -- Topics are likely to change a bit as we arrange with our guest speakers.
Please do give us feedback at any time on how we can make the unit work better! In particular, if you find some topics assume prior knowledge you don't have, or would like to go further into certain topics, let us know.
Please see below for some great all-round textbooks on AI, which may help you fill in some knowledge gaps...
We also have a Team on MS Teams. Please do use it to ask us questions, discuss AI topics you are interested in, post interesting blogs, videos or papers, etc...
Attention and Transformers. Both of the following are really good introductions to attention and transformers
- you can pick one to read, or go through both if that helps your understanding. Please cover one of these before Monday:
Data and labels for image and Video understanding:
ImageNet, the competition that kick-started the deep learning revolution in Computer Vision
Kitti, a highly impactful dataset in vision and robotics.
Optional extra: The winner of the ImageNet 2015 competition, ResNet.
Dima Damen -- tutorial on data and labels for video understanding
ES
5 (w/c 20/10/25)
Philosophy of AI
TBC
JC
6 (w/c 27/10/25)
CONSOLIDATION WEEK
(no seminar/reading group)
7 (w/c 03/11/25)
Knowledge representation and reasoning
TBC
JC
8 (w/c 10/11/25)
Bayesian inference and decision making
TBC
JC
9 (w/c 17/11/25)
AI for Health
Qiang Liu
ES
10 (w/c 24/11/25)
Bias, fairness and transparency
TBC
JC
11 (w/c 01/12/25)
Ethical and regulatory frameworks of AI
TBC
JC
12 (w/c 08/12/25)
Assessment period
TB1 essay due this week
TB2
13 (w/c 19/01/26)
TBC: Causality or Discrete and continuous optimisation?
James Cussens
JC
14 (w/c 26/01/26)
Reinforcement learning
TBC
ES
15 (w/c 02/02/26)
Robotics (TBC)
TBC
ES
16 (w/c 09/02/26)
Multi-agent systems
TBC
JC
17 (w/c 16/02/26)
Weak supervision
TBC
ES
18 (w/c 23/02/26)
CONSOLIDATION WEEK
No seminar/reading group
19 (w/c 02/03/26)
Learning from Temporal Data for Healthcare (TBC)
TBC
JC
20 (w/c 09/03/26)
Explainable and interpretable AI
TBC
ES
21 (w/c 16/03/26)
Privacy
TBC
JC
Easter Vacation (w/c 23/03/26)
22 (w/c 13/04/26)
Robust AI or MLOps and deploying AI in production (TBC)
TBC
ES
23 (w/c 20/04/26)
Human-in-the-loop AI -- design and evaluation:
TBC
ES
Assessment Details
Deadlines: at end of TB1 and end of TB2, dates TBC
After each Teaching Block students submit an essay of about 5,000 words (10 pages) on a research topic jointly chosen by them and their Academic Mentor.
The essay should describe the background, state of the art, and open challenges with regard to the chosen topic.
Each essay is assessed on a pass/fail basis in terms of scholarly content and academic writing.
Narrative feedback is also provided, indicating strong points as well as areas for improvement. Passing the unit requires passing both essays.
More guidance will be provided by the unit lecturers during the first term.
Text books
Bishop, C. M., Pattern recognition and machine learning (2006).
This is one of the best ML textbooks and will provide a solid foundation across many aspects of ML.
The book is freely available here.
Russell, S. and Norvig, P., Artificial Intelligence, A Modern Approach, 4th Edition (2020).
The canonical introduction to AI, 3rd edition available at here.
Jurafsky, D. and Martin, J.H., Speech and Language Processing, 3rd edition drafts (2024). A great NLP textbook, but very readable and
an interesting way to think about the challenges of designing AI systems in general. Also good for showing a contrast between deep learning
and feature engineering approaches. Online only, here.
Murphy, K., Probabilistic Machine Learning: An
Introduction (2022) and Murphy, K., Probabilistic
Machine Learning: Advanced Topics (2023). A more recent ML
textbook with particularly good coverage of
probabilistic methods, freely available via here.