- UCAS course code
- GG14
- UCAS institution code
- M20
Bachelor of Science (BSc)
BSc Computer Science and Mathematics
- Typical A-level offer: A*A*A including specific subjects
- Typical contextual A-level offer: AAA including specific subjects
- Refugee/care-experienced offer: AAB including specific subjects
- Typical International Baccalaureate offer: 38 points overall with 7,7,6 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 £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 Processing
Unit code | COMP34711 |
---|---|
Credit rating | 10 |
Unit level | Level 3 |
Teaching period(s) | Semester 1 |
Available as a free choice unit? | No |
Overview
This course unit will cover the key linguistic and algorithmic foundations of natural language processing. It will explore the main challenges in representing, searching and retrieving written documents, representing word semantics, and processing and identifying patterns in speech. It will consider both rule-based methods and machine/deep learning methods, and introduce key applications such as text information retrieval, text classification, word sense disambiguation, speech synthesis and speech recognition.
Pre/co-requisites
Unit title | Unit code | Requirement type | Description |
---|---|---|---|
Machine Learning | COMP24112 | Pre-Requisite | Compulsory |
Students taking this unit in 22/23 will be provided materials from COMP24112 if they have not taken it
Aims
Enabling computers to process data in 'natural language' (the kind of language that people use to communicate with one another) is becoming more and more important. It allows both people to communicate with computers, and the computers to access the enormous amount of material that is stored as natural language text on the web or in document repositories. This course unit provides an introduction to the area of natural language processing as one of the key areas of artificial intelligence. It aims to introduce essential components and key applications of natural language processing, and explain the major challenges in processing large-scale, real-world natural language both in its written and spoken forms.
Learning outcomes
- Discuss the major challenges in processing large-scale, real-world natural language data.
- Explain how the essential components of NLP systems are built, assessed and modified, including basic lexical and semantic processing approaches.
- Explain the use of basic statistical approaches, machine/deep learning techniques in building NLP systems.
- Discuss and implement key NLP tasks and applications, including document indexing, search and classification, word sense disambiguation.
- Explain the principal approaches and applications for speech synthesis and recognition.
- Understand the issues involved in deploying and evaluating NLP systems.
Syllabus
- Introduction to NLP
- Simple Language Models
- Information Retrieval
- Lexical Processing
- Word Semantics I: Word Sense Disambiguation
- Word Semantics II: Distributional Semantics
- Speech Synthesis
- Speech Recognition
- Deep Learning for NLP
- Ethical Considerations for NLP
Teaching and learning methods
Weekly workshops/lectures with structured input and exploratory activities. These will be organised as a blend of brief presentations, hands-on individual and group activities and discussions of materials and tasks that are available online (question-answer sessions).
Weekly laboratories will be individual and group hands-on sessions for trying out new systems or techniques (with set tasks, known answers) and will be also used for preparation for course work. These will be used as surgeries to provide feedback on coursework and as an opportunity to ask questions about the set tasks with more open ended/specific discussions and feedback.
Coursework will provide design, implement and analysis tasks with real-world data.
Assessment methods
Method | Weight |
---|---|
Written exam | 70% |
Written assignment (inc essay) | 30% |
Feedback methods
There will be both face-to-face feedback provided in workshops and tutorial and lab sessions, and written feedback provided through Blackboard discussion forum.
Study hours
Scheduled activity hours | |
---|---|
Lectures | 22 |
Practical classes & workshops | 11 |
Independent study hours | |
---|---|
Independent study | 67 |
Teaching staff
Staff member | Role |
---|---|
Goran Nenadic | Unit coordinator |