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 (TB1: Mondays, 1500-1600, SRB 5.401) -- a guest speaker will present an introduction to their research area, connected to the week's topic
Reading group discussion (TB1: Fridays, SRB 2.415) -- 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).
Please use the spreadsheet of session chairs to sign up for 1-2 sessions.
AI Lab Lunch and Learn Seminars: (see mailing list) -- 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 -- the topics for later in TB1 and TB2 will change as we arrange dates 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...
Optional : Tutorial from HuggingFace: hey have many other resources (models, datasets, transformers library, tutorials) that you can explore. You can even go through their
course.
Edwin Simpson
ES
2 (w/c 28/09/26)
Attention and Transformers.
Some introductory reading recommendations:
Deadlines: TB1 assessment = week of 14th December, exact time and date to be announced later. TB2 assessment = week of 4th May, exact time and date to be announced later.
More detailed guidance on the assessment can be found here
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.