Bachelor of Science (BSc)
BSc Actuarial Science and Mathematics
Duration: 3 years
Year of entry: 2025
UCAS course code: NG31 / Institution code: M20
- Key features:
Scholarships available
Accredited course
- Typical A-level offer: A*AA including specific subjects
- Typical contextual A-level offer: A*AB including specific subjects
- Refugee/care-experienced offer: A*BB including specific subjects
- Typical International Baccalaureate offer: 37 points overall with 7,6,6 at HL, including specific requirements
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).
Scholarships/sponsorships
The University of Manchester is committed to attracting and supporting the very best students. We have a focus on nurturing talent and ability and we want to make sure that you have the opportunity to study here, regardless of your financial circumstances.
For information about scholarships and bursaries please visit our undergraduate student finance pages and our Department funding pages .
Course unit details:
Introduction to AI
| 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 |
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
- 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
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
Recommended reading
COMP24011 reading list can be found on the Department of Computer Science website for current students.
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 |