School of Engineering, Computing and Mathematics

BSc (Hons) Computer Science (Artificial Intelligence)

UCAS tariff 120 - 128
UCAS course code I400
Institution code P60
Duration 3 years (+ optional placement)
Course type Full-time
Location Plymouth

Artificial intelligence (AI) is a rapidly growing field within computer science, and this course will give you the skills you need to excel within it. Topics will range from real-word applications of AI to understanding the theoretical underpinnings of the subject, to creating the innovative AI-driven tools that will drive Industry 4.0. You will also focus on the wider ethical and societal implications of AI.

Taught by a team of internationally recognised AI experts with extensive research and commercial experience that is drawn upon in a research-led teaching experience; and providing students with an exciting opportunity to engage with world-leading research.

Computer Science (Artificial Intelligence)

Image by Alan Warburton / © BBC / Better Images of AI / Nature / CC-BY 4.0

Careers with this subject

Artificial intelligence graduates have gained employment as:

  • mobile developers
  • web application developers
  • software developers
  • project managers
  • systems analysts
  • software engineers
  • UX developer
  • data analytics developer
  • AI developer
  • machine learning engineer
  • data engineer/architect.

Key features

  • Be inspired by the creativity that our practical, hands-on course nurtures
    Our ‘learning through doing’ ethos means you build the skills to make you desirable to employers. You’ll learn from dedicated teaching staff engaged in internationally significant research, actively creating and developing applications.

  • Collaborate and build
    Mirroring the teamwork at the heart of the industry, you’ll work in a team to develop work with a real purpose.

  • Get a head-start in the industry
    Benefit from a programme with strong links with industry, (e.g. Apple, Microsoft, IBM, Oracle and Intel) and those links are used to embed relevant real-world problems directly into the programmes.

Course details
  • Year 1

  • Core modules

    • Stage 1 Computing Placement Preparation (BPIE111)

      This module is aimed at students who may be undertaking an industrial placement in the third year of their programme. It is designed to assist students in their search for a placement and in their preparation for the placement itself.

    • Software Engineering 1 (COMP1000)

      This module exposes students to the principles of software design and construction. The basics of constructing source code to solve a problem will be introduced, exposing students to common control structures alongside concepts such as types and generics. Major programming paradigms such as object orientation and functional programming are introduced. Additionally, key software development tools and methods are explored.

    • Computer Systems (COMP1001)

      This module provides students with an underpinning knowledge of how computers work. Topics include low-level systems and representation of data, operating systems, and an introduction to subjects such as virtualisation, parallelism, state and communications. Students will learn how operating systems manage processes and scheduling, and how memory management works.

    • Cyber Security & Networks (COMP1002)

      Modern computing relies upon networking and robust cyber security. This module provides an appreciation of their core enabling technologies, discussing how they can be applied. Key networking topics include routing and switching, as well as wireless networks. Key areas of security include underlying concepts and threats, and exploring security technologies that can be applied to enable defence in depth.

    • Algorithms, Data Structures and Mathematics (COMP1003)

      Data structures and algorithms lie at the heart of Computer Science as they are the basis for an efficient solution of programming tasks. In this module, students will study core algorithms and data structures, as well as being given an introduction to algorithm analysis and basic Mathematics for Computer Science.

    • Computing Practice (COMP1004)

      This module applies problem-based learning to provide students with the ability to identify problems and derive appropriate and considered solutions. A focus will be given to the early stages of the software development lifecycle to develop the skills of eliciting requirements whilst considering operational and technical trade-offs. The module will culminate in the creation of a simple yet complete software solution.

  • Year 2

  • Core modules

    • Stage 2 Computing Placement Preparation (BPIE211)

      This module is aimed at students who may be undertaking an industrial placement in the third year of their programme. It is designed build on the Level 1 module (BPIE111) and to assist students in their search for a placement and in their preparation for the placement itself.

    • Software Engineering 2 (COMP2000)

      Students’ understanding of software engineering is expanded by introducing a range of topics that instil best practice. Students will learn how to implement faster software using parallelism and consider aspects of human-computer interaction. Object-orientation and functional programming are revisited, while event-driven programming is introduced. Common design patterns used in the construction of software are introduced.

    • Information Management & Retrieval (COMP2001)

      This module introduces students to the fundamental concepts for graphical representation, information management, database systems and data modelling. The capture, digitisation, representation, organisation, transformation and presentation of information is explored using conceptual and physical data models.

    • Artificial Intelligence (COMP2002)

      This module provides students with an introduction to the principles of artificial intelligence and the methods used in that field. Topics covered include search and optimisation, knowledge representation and reasoning, and machine learning. Students will gain experience of modelling and simulation, and will apply analytical tools to evaluating results, and will consider the ethical implications of the introduction of AI.

    • Computing Group Project (COMP2003)

      Knowledge gained in earlier stages of the computing programmes is consolidated and integrated into a substantial project. Students work in teams, champion professional roles, design and develop a software solution for a given scenario. The project integrates and expands upon software development stages covered on the course (project management, analysis, design, construction, communication, security and/or networking).

    • Embedded Programming and the Internet of Things (COMP2008)

      Learn about embedded microcontrollers, work with different processor architectures and develop embedded software. The use of hardware peripherals, interrupts, multi-tasking and defensive programming will be explored and students will use programming strategies to optimize the execution time, energy consumption and memory size of their programs. The use of embedded programming within IoT applications is considered.

  • Year 3

  • Core modules

    • Computing Related Placement (Generic) (BPIE330)

      A 48-week period of professional training spent as the third year of a sandwich course, undertaking an approved placement with a suitable company. This provides an opportunity for you to gain relevant industrial experience to consolidate the first two years of study and to prepare for the final year and employment after graduation. Please note this placement is optional but strongly recommended.

  • Final year

  • Core modules

    • Computing Project (COMP3000)

      The Computing Project provides an opportunity to tackle a major computing related problem in an approved topic area relevant to the programme of study.

    • Machine Learning (COMP3003)

      This module introduces machine learning, covering unsupervised, supervised and reinforcement learning from a Bayesian perspective. This includes theory behind a range of learning techniques and how to apply these representations of data in systems that make decisions and predictions.

    • Artificial Intelligence and Robotics (COMP3018)

      This module provides the opportunity for students to acquire a critical understanding of state-of-the-art robotics tools and techniques. As well as exploring a range of real-world applications of robotics, students will learn about the practical issues around the use and construction of physical robots. Algorithms used in control will be discussed, and students will apply their skills using industry standard tools.

    Optional modules

    • Parallel Computing (COMP3001)

      This module develops an understanding of problems in Computer Science which take advantage of general-purpose computing on GPUs. It provides practical methodologies to reformulate problems in terms of hardware architecture, graphics primitives and high-performance computing concepts, as supported by the most recent GPUs. It develops the skills to implement parallel solutions with common GP-GPU computing languages.

    • Alternative Paradigms (COMP3002)

      Imperative programming and related “classic” machines like finite state or Turing machines dominate the field of computing. This module aims to expose students to ways of thinking about computational problems that go beyond mainstream imperative styles (e.g., functional and declarative programming) and to ideas and workings of and behind unconventional and upcoming computing paradigms (e.g. quantum or neural computing).

    • Full-Stack Development (COMP3006)

      This module explores the production of dynamic web applications with a particular focus on the web environment. Key elements such as object oriented and event-based development, asynchronous client-server communication and distributed content representation are explored through practical production. The production of a working system uses dynamic web frameworks such as HTML, CSS and JavaScript/jQuery.

    • Big Data Analytics (COMP3008)

      The key objective of this module is to familiarise the students with the most important information technologies used in manipulating, storing and analysing big data. Students will work with semi-structured datasets and choose appropriate storage structures for them. A representative of recent non-relational trends is presented—namely, graph-oriented databases.

Every undergraduate taught course has a detailed programme specification document describing the course aims, the course structure, the teaching and learning methods, the learning outcomes and the rules of assessment.

The following programme specification represents the latest course structure and may be subject to change:

BSc Computer Science Artificial Intelligence programme specification 7392

The modules shown for this course are those currently being studied by our students, or are proposed new modules. Please note that programme structures and individual modules are subject to amendment from time to time as part of the University’s curriculum enrichment programme and in line with changes in the University’s policies and requirements.

In light of the Coronavirus (COVID-19) pandemic, the changeable nature of the situation and any updates to government guidance, we may need to make further, last minute adjustments to how we deliver our teaching and learning on some or all of our programmes, at any time during the academic year. We want to reassure you that even if we do have to adjust the way in which we teach our programmes, we will be working to maintain the quality of the student learning experience and learning outcomes at all times.
Entry requirements

UCAS tariff

120 - 128


Applicants exceeding our entry requirements may be eligible for an offer under our Computing Excellence Scheme.

GCSE: English C/4 and mathematics grade B/5. If you have a grade C/4 in mathematics please contact admissions team.

A levels: A typical offer is 112-120 points from minimum of 2 A levels in any subject. Excluding general studies.

International Baccalaureate: 27-30 overall

18 Unit BTEC National Diploma/QCF Extended Diploma: DMM-DDM – science related subjects: IT, Humanities, Engineering, Software Development, IT Practitioners, Business, Computing, Science (GCSE English C/4 and mathematics grade B/5. If you have a grade C/4 in mathematics please contact admissions team).

BTEC National Diploma modules
If you hold a BTEC qualification it is vital that you provide our Admissions team with details of the exact modules you have studied as part of the BTEC. Without this information, we may be unable to process your application quickly and you could experience significant delays in the progress of your application to study with us. Please explicitly state the full list of modules within your qualification at the time of application.

All Access courses: 33 credits at merit and/or distinction and to include at least 12 level 3 credits in mathematics with merit. Including a minimum of GCSE English and Mathematics grade C/4. If mathematics not included please contact the admissions team at admissions@plymouth.ac.uk.

T level: Merit to Distinction depending on the Mathematics units studying within the T level pathways.

Other qualifications will be considered individually; please contact us for information.

English language requirements

We welcome applicants with international qualifications. To view other accepted qualifications please refer to our tariff glossary.

Fees, costs and funding

The UK is no longer part of the European Union. EU applicants should refer to our Brexit information to understand the implications.

New Student 2021-2022 2022-2023
Home N/A £9,250
International N/A £14,600
Part time (Home) N/A £770
Full time fees shown are per annum. Part time fees shown are per 10 credits. Please note that fees are reviewed on an annual basis. Fees and the conditions that apply to them shown in the prospectus are correct at the time of going to print. Fees shown on the web are the most up to date but are still subject to change in exceptional circumstances. More information about fees and funding.

The Plymouth Computing Scholarship

Students can obtain a Computing Scholarship of up to £1,000 by gaining two A grades at A level - that is £500 for each of the two A grades at A level. This is awarded to home applicants who put us as their firm choice before 1 August 2022. The full Computing Scholarship can also be obtained by students who have put us as their first choice by that date and have acquired three D* grades at BTEC level (this needs to be an IT and Computing-related BTEC). The scholarship is paid during your first year.

Undergraduate scholarships for international students

To reward outstanding achievement the University of Plymouth offers scholarship schemes to help towards funding your studies.

Find out whether you are eligible and how you can apply

Additional costs

This course is delivered by the Faculty of Science and Engineering and more details of any additional costs associated with the faculty's courses are listed on the following page: Additional fieldwork and equipment costs.

How to apply
All applications for undergraduate courses are made through UCAS (Universities and Colleges Admissions Service). 

UCAS will ask for the information contained in the box at the top of this course page including the UCAS course code and the institution code. 

To apply for this course and for more information about submitting an application including application deadline dates, please visit the UCAS website.

Support is also available to overseas students applying to the University from our International Office via our how to apply webpage or email admissions@plymouth.ac.uk.

Preparing coders, creators and developers for work

<p>Student Lucinda builds apps for tablets. Lucy currently does a lot of volunteering and tech jams and code clubs to support younger people.<br></p>

"We regularly have students securing placements and jobs at FTSE 100 companies. There is a perception that only students from red brick universities go to these places. It simply is not the case – our students are massively competitive and win these placements as well." 

Professor Nathan Clarke
Deputy Head of School (School of Engineering, Computing and Mathematics)

The placement year taught me how to interact with different people throughout the company at differing levels and how to approach conversations to get the maximum amount of information out of them.

Richard Imms, BSc (Hons) Computer Science graduate, Senior Machine Learning Engineer at Just Eat

Placement years: use your knowledge in the workplace

Learn from researchers and lecturers who are developing applications

Benefit from our industry expertise and study a course that is influenced by our innovative research. Study theoretical and practical modules, which cover a range of topics:

  • machine learning
  • computational theory
  • artificial intelligence
  • computer vision
  • parallel computing.

Shutterstock image, close up of hand using a touch screen to access data

Research-informed teaching

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