COMS20018 - Introduction to Artificial Intelligence

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Unit Information

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.

Staff

Mengyan Zhang (Unit Director), Nan Lu, James Cussens

Teaching Assistants

Yuming Li, Siddhant Bansal, Aswathi Thrivikraman

Unit Materials

Lecture slides and lab notebooks will be linked here as they become available.

Schedule

Lecture 1: Mondays, 1100-1200, Fry Building G.09
Lecture 2: Tuesdays, 1500-1600, Fry Building G.09
Lab/Seminar: Wednesdays, 1100-1300, Merchant Venturer's Building 1.15 (Linux Computer Lab)

Week	Topic	Lecturer	Lecture 1 (Mon, 1100-1200)	Lecture 2 (Tue, 1500-1600)	Lab/Seminar (Wed, 1100-1300)
1 (w/c 21/09/26)	Introduction	MZ	Unit introduction	Introduction to AI	Introduction to Python
2 (w/c 28/09/26)	Machine Learning	MZ	Linear Regression and Classification	Neural Networks: Introduction	Lab: Linear Regression and Classification
3 (w/c 05/10/26)	Deep Learning	NL / MZ	Neural Networks: CNNs, RNNs, and Transformers (MZ)	Generative Models	Lab: Neural Networks
4 (w/c 12/10/26)	Large Language Models	NL	Large Language Models	Large Language Models	Lab: Large Language Models
5 (w/c 19/10/26)	Philosophy of AI	JC	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	NL	Ethics	Ethics	Seminar
8 (w/c 09/11/26)	Causal Inference	MZ	Causal Machine Learning	Causal Decision Making	Lab: Causal Machine Learning
9 (w/c 16/11/26)	Reinforcement Learning	MZ	Multi-Armed Bandits	Reinforcement Learning	Lab: Reinforcement Learning
10 (w/c 23/11/26)	Computer Vision	NL / Guest	Computer Vision	Computer Vision (Guest lecture)	Lab
11 (w/c 30/11/26)	Trustworthy AI	NL	Trustworthy AI	Trustworthy AI	Lab/Seminar
12 (w/c 07/12/26)	Revision				No lab
13 (w/c 14/12/26)	Final Exam period

Assessment Details

Students will be assessed via three elements:

A mid-term video presentation (worth 20% of the unit)
A series of laboratory-based assessments (worth 20% of the unit)
An end-of-term exam (worth 60% of the unit)

Lab Work

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.

Using the machines in MVB 1.15

If you are using UoB machines to do the lab exercises (as opposed to using your own machine) you need to do the following.

Make sure the machine you are using is running Linux (reboot if necessary).
Open up a Terminal window so you have access to the Linux command line.
Enter the following at the command line: module load anaconda/3-2025

Using your own machine

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.

Reading Materials

Readings are listed below for weeks with finalised lecture content; materials for the remaining weeks will be added as those lectures are finalised.

Week 2: Machine Learning

Bishop, C. M., Pattern Recognition and Machine Learning (2006) – our main textbook for Weeks 2–3. Free online. Chapter 1 (§1.1), Chapter 3 (§3.1), Chapter 4 (§4.3.2), Chapter 5: (§5.1, §5.2, §5.3). Free online.

Week 3: Deep Learning

Bishop & Bishop, Deep Learning: Foundations and Concepts (2024)
Goodfellow, Bengio, Courville, Deep Learning (2016), Chapters 6 and 8 – a more modern treatment (ReLU, Adam, and optimisation for deep nets). Free online (optional, further reading).
Vaswani et al. “Attention Is All You Need.” NeurIPS, 2017 (the original Transformer paper).

Week 8: Causal Inference

Hernán & Robins, Causal Inference: What If (2020), Part I (Chapters 1–7). Free online.
Spirtes, Glymour, Scheines, Causation, Prediction, and Search (2000), Chapter 5 (constraint-based discovery), or Peters, Janzing, Schölkopf, Elements of Causal Inference (2017), Chapter 7 (overview). Free online (Peters et al.).

Week 9: Reinforcement Learning

Sutton & Barto, Reinforcement Learning: An Introduction (2nd ed.), Chapter 2: Multi-armed Bandits. Free online.
Lattimore & Szepesvári, Bandit Algorithms (2020), Chapters 1, 2 and 19 (linear bandits). Free online.
Li, Chu, Langford, Schapire. “A Contextual-Bandit Approach to Personalized News Article Recommendation.” WWW 2010 (for LinUCB).
Sutton & Barto, Reinforcement Learning: An Introduction (2nd ed.), Chapters 3 (MDPs), 6 (TD learning & Q-learning) and 13 (Policy Gradient methods). Free online.