Bachelor of Science (BSc)

BSc Mathematics with Placement Year

Strengthen your employability by taking our Placement Year programme.
  • Duration: 4 years
  • Year of entry: 2025
  • UCAS course code: G101 / Institution code: M20
  • Key features:
  • Industrial experience
  • Accredited course

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Fees and funding

Fees

Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £34,500 per annum. For general information please see the undergraduate finance pages.

Policy on additional costs

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

Course unit details:
Introduction to AI

Course unit fact file
Unit code COMP24011
Credit rating 10
Unit level Level 2
Teaching period(s) Semester 1
Available as a free choice unit? Yes

Overview

The Unit aims to make students familiar with the basic concepts and techniques of Artificial Intelligence. It provides the knowledge and understanding that underpins later course units in the subject taught in the Department.

Pre/co-requisites

Unit title Unit code Requirement type Description
Mathematical Techniques for Computer Science COMP11120 Pre-Requisite Compulsory
Data Science COMP13212 Pre-Requisite Compulsory
Students who are not from the Department of Computer Science must have permission from both Computer Science and their home School to enrol.

COMP11120 (not a pre-requisite for CM)

Aims

A student completing this course should:

  • be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems;
  • be able to apply first-order logic to model physical situations and reason about the effects of actions,
  • to appreciate the limitations of logic and to select appropriate responses to these limitations;
  • be able to develop formal ontologies to represent knowledge in different domains;
  • be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations;
  • be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics;
  • be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence;
  • have an appreciation of the central philosophical problems connected with artificial intelligence.

Learning outcomes

On the successful completion of the course, students will be able to: 
  • ILO 1 To be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems.
  • ILO 2 To be able to apply first-order logic to model physical situations and to reason about the effects of actions.
  • ILO 3 To appreciate the limitations of logic and to be able to select appropriate responses to these limitations.
  • ILO 4 To be able to develop formal ontologies to represent knowledge in different domains.
  • ILO 5 To be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations.
  • ILO 6 To be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics.
  • ILO 7 To be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence.
 

Syllabus

Topic 1. Search and planning:
   problem-solving as search; adversarial games; classical planning.
Topic 2. Logic and reasoning
   review of first-order logic; applications of logic to planning; logic versus reasoning; default `logic'.
Topic 3. AI and probability
   review of probability theory; alternative representations of uncertainly; Bayes' networks.
Topic 4. Knowledge representation
   ontology-driven database access; formal ontologies and knowledge-representation.
Topic 5. The periphery:
   sensors and actuators; sensor integration, simultaneous localization and mapping.
Topic 6. Philosophical issues:
   the Turing test; the meaning of `AI'; the problem of consciousness.

Teaching and learning methods

2 hours lectures per week (22 hours in total), 2 hours of lab per fortnight (8 hours in total)

Assessment methods

Method Weight
Written exam 80%
Written assignment (inc essay) 20%

Feedback methods

Exam and assessments

Coursework:
 
Lab 1: Games
Lab 2: Constraints 
Lab 3: SLAM 
Lab 4: BM25

Recommended reading

COMP24011 reading list can be found on the Department of Computer Science website for current students.

Stuart Russell and Peter Norvig: Artificial Intelligence: A Modern Approach, Global Edition, Pearson, 2016
 
Ronald Brachman: Knowledge representation and reasoning, Morgan Kaufmann, 2004
 
Simon J. D. Prince: Computer vision : models, learning, and inference, Cambridge University Press, 2012.
 

Study hours

Scheduled activity hours
Lectures 22
Practical classes & workshops 8
Independent study hours
Independent study 70

Teaching staff

Staff member Role
Ian Pratt-Hartmann Unit coordinator

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