Bachelor of Science (BSc)

BSc Computer Science and Mathematics with Industrial Experience

Graduate this highly sought-after subject combination having already gained invaluable experience in industry.
  • Duration: 4 years
  • Year of entry: 2025
  • UCAS course code: GG41 / Institution code: M20
  • Key features:
  • Industrial experience
  • Scholarships available

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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 £36,000 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 .

Course unit details:
Natural Language Understanding

Course unit fact file
Unit code COMP34812
Credit rating 10
Unit level Level 3
Teaching period(s) Semester 2
Available as a free choice unit? No

Overview

Drawing from concepts covered in the prerequisite COMP34711: Natural Language Processing unit, this unit will enable students to look more deeply into how machines analyse and recognise meaning expressed in natural language. In this unit, students will gain hands-on experience in investigating solutions to a number of natural language understanding tasks. This will provide students with the know-how required to develop technologies for real-world applications enabling communication between humans and machines, which have become increasingly ubiquitous and indispensable.

Pre/co-requisites

Unit title Unit code Requirement type Description
Natural Language Processing COMP34711 Pre-Requisite Compulsory

Aims

The unit aims to:

- introduce students to the concepts and computational methods that enable machines to understand and interpret natural language

-  explain the various tasks that underpin natural language understanding, and provide an overview of the state-of-the-art solutions to these tasks as well as their real-world applications

Learning outcomes

  • To discuss the formulation of different natural language understanding tasks as sequence processing tasks e.g., sequence classification, sequence-to-sequence translation and sequence labelling.
  • To differentiate between different types of parsing algorithms and apply them to natural language data to produce meaning representations.
  • To compare different approaches to tasks such as named entity recognition and sentiment analysis.
  • To relate natural language understanding tasks to applications such as question answering and conversational agents, among others.
  • To develop a solution to a natural language understanding task with application to a real-world problem

Syllabus

  • Introduction to NLU; Task formulations and applications
  • Meaning representations: symbolic parsing and logical representations of sentences
  • Vector-based representations (contextualised embeddings)
  • Neural networks and neural language models
  • Evaluation of models
  • Sequence classification and textual entailment (and applications)
  • Sequence labelling (and applications)
  • Machine reading comprehension (and applications)
  • Sequence-to-sequence translation (and applications)
  • Limits and weaknesses of state-of-the-art approaches to NLU

Teaching and learning methods

Asynchronous lectures (weekly)

Synchronous workshops (weekly)

Labs (fortnightly)

Employability skills

Analytical skills
Problem solving
Written communication

Assessment methods

Method Weight
Written exam 70%
Practical skills assessment 30%

Feedback methods

Discussions and live coding during workshops (weekly)

Labs to support coursework (fortnightly)

Cohort-level feedback on exam

Study hours

Scheduled activity hours
Lectures 20
Practical classes & workshops 10
Independent study hours
Independent study 70

Teaching staff

Staff member Role
Riza Batista-Navarro Unit coordinator

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