Artificial Intelligence and Automata - COM00217M

«Back to module search

Module summary

This is an optional 20-credit module available to MSc Data Science students who have not studied AI previously.

This module begins by revising and extending fundamental skills and knowledge in programming, algorithms, and discrete and continuous mathematics, including automata, which are required for further study in AI. While students may have encountered some of this material to varying extents and depths in their prior studies, the module ensures that all students develop a shared, robust foundation of knowledge and skills across these core areas. The module then introduces the philosophical and ethical foundations of intelligence and artificial intelligence, explores the major AI paradigms, and provides an overview of symbolic, statistical, and learning-based approaches to AI.

Module will run

Occurrence	Teaching period
A	Semester 1 2026-27

Module aims

This module aims to lay the foundation for advanced study in symbolic, statistical and learning-based approaches to artificial intelligence. It revises and extends fundamental skills and knowledge in programming, algorithms, and discrete and continuous mathematics including automata, from the perspective of their use in AI. It introduces the philosophical and ethical basis of intelligence and AI, the different paradigms of AI and some basic approaches within these paradigms. Practical work will involve both mathematical exercises and programming in Python.

Module learning outcomes

• Explain basic philosophical arguments and categorisations relating to notions of intelligence, computation and artificial intelligence

• Construct finite automata and pushdown automata, and analyse the classes of languages they can and cannot recognise

• Transform and convert between different models of automata, and between automata and grammars, for regular and context-free languages

• Distinguish between symbolic and learning-based AI and between supervised and unsupervised learning

• Assess the suitability of regular languages and context-free languages for AI tasks

• Represent knowledge using propositional and first order logic

• Compute partial derivatives and solve systems of linear equations using linear algebra

• Solve regression and classification problems with one variable or multiple variables using linear and logistic regression

• Apply the gradient descent algorithm to optimise multivariate functions

• Construct a basic neural network and optimise its weights using backpropagation

• Assess the appropriateness of machine learning methods for AI applications

Module content

• Introduction to AI

• Automata and formal languages

• Search and representation

• Logics

• Introduction to machine learning and clustering

• Linear algebra for machine learning

• Mathematics for gradient descent

• Linear models (linear regression, logistic regression) and regularisation

• Neural networks and backpropagation

Indicative assessment

Task	% of module mark
Closed/in-person Exam (Centrally scheduled)	100.0

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Closed/in-person Exam (Centrally scheduled)	100.0

Module feedback

Feedback is provided through work in practical sessions, weekly online tests, a practical exam, and after the final assessment as per normal University guidelines.

Indicative reading

• Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed., Global ed.). Pearson.

• Haykin, S. S. (2009). Neural Networks and Learning Machines (3rd ed.). Pearson Education, Upper Saddle River, NJ.

• Linz, P., & Rodger, S. H. (2022). An introduction to formal languages and automata (7th ed.). Jones & Bartlett Learning.