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

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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).

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:
Logic and Modelling

Course unit fact file
Unit code COMP21111
Credit rating 10
Unit level Level 2
Teaching period(s) Semester 1
Available as a free choice unit? Yes

Overview

This is a unique course developed at the University of Manchester. It explains how implementations of logic can be used to solve a number a number of problems, such as solving hardest Sudoku puzzles in no time, analysing two-player games, or finding serious errors in computer systems

Pre/co-requisites

Unit title Unit code Requirement type Description
Mathematical Techniques for Computer Science COMP11120 Pre-Requisite Compulsory
Linear Algebra MATH11022 Pre-Requisite Compulsory
Mathematical Foundations & Analysis MATH11121 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. Students must have taken either COMP11120 or MATH11121 and MATH11022.

Pre-requisites

To enrol students are required to have taken COMP11120 or one of the following: MATH10111, MATH10131 , MATH10212, MATH10232.

Aims

This course intends to build an understanding of fundamentals of (mathematical) logic as well as some of the applications of logic in modern computer science, including hardware verification, finite domain constraint satisfaction and verification of concurrent systems.

Learning outcomes

  • Have a knowledge about basic reasoning (or satisfiability-checking) algorithms for propositional logic.

  • Have a knowledge of quantified boolean formulas and basic understanding of bound variables and quantifiers.

  • To understand BDDS (binary decision diagrams) as a data structure for compact representation of propositional formulas.

  • Have a knowledge about applications of propositional logic (such as finite domain constraint satisfaction and planning) and be able to apply it for solving hard combinatorial problems.

  • Have a knowledge of simple temporal logics.

  • Be able to formally specify finite-state concurrent systems as transition systems.

  • Be able to specify properties of simple transition systems in temporal logics.

Syllabus

  • Propositional logic
  • Conjunctive normal form (CNF)
  • DPLL satisfiability algorithm
  • Randomized satisfiability algorithms
  • Compact representations of Boolean functions using BDTs/BDDs/OBDDs
  • Quantified Boolean Logic (QBF) Splitting and DPLL algorithms for QBF 
  • Propositional logic of finite domains
  • State-changing systems 
  • Linear temporal logic (LTL)
  • Model checking

Teaching and learning methods

Lectures

22 in total, 2 per week, including some feedback sessions on exercises

Employability skills

Analytical skills
Innovation/creativity
Problem solving
Research

Assessment methods

Method Weight
Written exam 50%
Written assignment (inc essay) 50%

Feedback methods

My Website of this course will contain a lot of material, including solutions to exercises

Recommended reading

COMP21111 reading list can be found on the Department of Computer Science website for current students.

Study hours

Scheduled activity hours
Assessment written exam 2
Lectures 24
Practical classes & workshops 9
Independent study hours
Independent study 65

Teaching staff

Staff member Role
Konstantin Korovin Unit coordinator

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.

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