Introductory description

Introduction to the main toolbox of modern AI and ML, touching upon the basics of supervised and unsupervised learning, delving deeper into Reinforcement Learning. The module serves as a taster for students considering further specialisation in Artificial Intelligence and Machine Learning .

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Intro to AI: challenges, applications, ethics, explainability, transparency, fairness

Basics of Search and Decisions (Tree/Graph Search, Local Search, Adversarial Search)

Intro to Machine Learning and Deep Learning (Supervised and Unsupervised Learning, Learning Concepts from Examples, Classification and Regression Trees, Intro to Bayesian Networks, Intro to Neural Networks, Clustering.

Basics of Reinforcement Learning (Agents, Policies, Optimisation, Value/policy Iteration, Temporal Difference Learning, SARSA, Q-learning)

RL with Human Feedback, Transformers and intro to LLMs.

Learning outcomes

By the end of the module, students should be able to:

• A systematic understanding of the main algorithms used in modern AI research and practice.
• The capacity to apply and execute the main Machine Learning techniques for supervised and unsupervised learning, comparing and optimising their use against several applications.
• Design tailor-made learning algorithm for a specific problem.
• An appreciation of responsible and explainable AI, ethical considerations, and evaluating limitations of the technology.

Indicative reading list

Please see Talis Aspire link for most up to date list.

Research element

Coursework will include a research element.

Subject specific skills

In line with the learning objectives of the module, students will acquire:

A systematic understanding of the main algorithms used in modern AI research and practice;
The capacity to apply and execute the main Machine Learning techniques for supervised and unsupervised learning, comparing and optimising their use against several applications;
Skills to design tailor-made learning algorithm for a specific problem;
An appreciation of responsible and explainable AI, ethical considerations, and evaluating limitations of the technology.

Transferable skills

Being able to apply knowledge and understanding of specialist theoretical and methodological approaches in Artificial Intelligence and Machine Learning, suggesting and incorporating interrelationships with other relevant disciplines in abstract and unpredictably complex contexts.

Students will obtain the cognitive skills to critically contribute to existing discourses and methodologies in Artificial Intelligence and Machine Learning, suggesting new ideas, and designing systematic studies in this area, based on critical analysis and evaluation.

Students will obtain practical skills in organising and communicating information, improving interpersonal, team
and networking skills through engaging in classes and computer laboratories. Formative assessment will allow students to strategically enhance their own learning.

Artificial Intelligence and Machine Learning are areas with immediate relevance for increasing ethical awareness and its practical application regarding privacy concerns and AI alignment. The associated values will help understanding the importance of personal responsibility and ethical leadership.