Clearing offers from 56 UCAS tariff points. Subject-specific requirements still apply. See the entry requirements section for details.
Brayford Pool
4 years
G412
Artificial intelligence is changing how organisations work, from healthcare, agriculture, and finance to gaming, robotics, and cybersecurity. It is the area of computer science focused on building intelligent systems that can make decisions, solve problems, and learn from data.
Our MComp Computer Science with Artificial Intelligence programme combines core computer science with specialist AI study. The first three years of this integrated Master's programme are identical to our BSc Computer Science with Artificial Intelligence programme. In the fourth year, you will explore master's-level topics and complete a research-driven project with real- world applicability.
If you enjoy technology, coding, data, logic, and problem-solving, this course can help you build advanced technical skills for a fast-moving digital future.
This course is designed to help you understand both the foundations of computer science and the specialist techniques used to build artificial intelligence systems.
In your first year, you can develop core skills in writing code, buildingsoftware and designing computers.
In your second year, you can build on this foundation through building software systems, developing AI and designing computer networks.
In your third year, you can explore more advanced and specialist areas including deploying secure software systems, developing mobile applications and training deep learning models.
In the fourth year, you will study master's level modules on machine learning mand artificial intelligence, and gain experience of working on a research-driven project.
Throughout the programme your will develop your teamwork, individual work and problem-solving skills, helping you to develop your career readiness. You'll have the opportunity to complete a substantial individual project on a topic that interests you, under the supervision of an academic.
This module aims to equip students with an understanding of time and space efficiency, enabling them to select appropriate algorithms for the programming problems they are presented with. Students will be introduced to relevant theoretical concepts around algorithms and data structures in lectures, together with practical experience of implementing them in the workshops.
This module introduces the fundamentals of computer hardware. You will be provided with the knowledge of how core computer components function and how they come together to form a single system. The module will introduce data representation and digital logic, followed by a study of the Central Processing Unit, memory, interconnections and I/O devices. Standard, sequential (i.e. von Neumann) architecture will be compared to modern hardware platforms that are based on multi-core processors, parallel units and embedded systems.
Data science is a relatively new field of study that utilises algorithms, statistics, and visualisation methods to answer scientific questions using data. In this module, students learn how to load, transform, visualise, and extract knowledge from data using their skills as programmers. Students can also gain experience in using interactive programming environments (e.g. IPython/Jupyter) and open-source libraries (e.g., numpy, matplotlib, pandas) that are widely used by data scientists in industry. In the latter part of the module, students will work in groups to analyse a real-world dataset and present their findings to their peers.
This module introduces students to software constructs and the development of programs using a high-level programming language. Students will learn about standard programming practices and develop software using the object-oriented programming paradigm. Attention is paid to the fundamentals that constitute a complete computer program including layout, structure, and functionality. There is also emphasis upon the use of debugging tools and unit testing.
This module will outline the main components of the software design and development process that ensure software is fit for purpose and of sufficient quality. Students will develop their practical understanding and appreciation of frameworks for software development processes using case studies and practical implementations.
In industry, computer scientists and software developers work in teams to create solutions to a variety of different problems. This module aims to introduce the art of problem solving, teamwork, and the industry employment process to help equip students with the skillsets required for an industry setting.
This module offers a hands‑on introduction to the core concepts of machine learning, showing you how intelligent systems learn from data to make predictions and uncover hidden patterns. Through practical work with both supervised and unsupervised methods, you will build the skills needed to tackle real‑world data science challenges and apply machine learning techniques across a wide range of domains.
The module aims to provide a modern introduction to the concepts of symbolic artificial intelligence, set in the context of intelligent agents.
The module covers the concepts such as state space representations and search, heuristic and adversarial search methods, and optimization techniques. The module also covers knowledge representation, AI planning, and some nonstatistical, machine learning methods.
This module will explore the ‘full stack’ of web application technologies. You will have the opportunity to learn how to design and develop both the frontend and backend of modern web applications. The module aims to cover the three-tier architecture approach for developing web applications: i) presentation tier, ii) application tier, and iii) data tier. You can learn how to use the relevant technologies for each tier, encompassing web presentation, application programmable interfaces (APIs), and database technologies. The overall aim of the module is for you to learn the how to develop robust client-server applications using secure and scalable technologies.
In this module, students learn how computers can be used to analyse and process the natural language that we use in our everyday lives. Natural language is a data type like no other, and presents a unique set of challenges for which the field of Natural Language Processing (NLP) has sought to provide answers. Common applications of NLP include machine translation, text summarisation, question answering, chatbots, grammar checking, and many others.
This module considers basic computer communications and networking with an emphasis on the Internet Protocol.
The module examines the Internet Protocol as a model for intercommunication in modern network implementations. Additionally the module examines fundamental design features of a Network Protocol and the need to implement security in the modern Internet.
The module adopts a standards driven approach and determines methods used in modern network systems for the distribution of data. An emphasis on network infrastructure and protocols underpins the module together with basic security considerations important in modern network architectures. Aspects of security concepts are extended to consider mechanisms that counter various forms of threat that exist from different sources.
This purpose of this module is to provide students with the experience of working as part of a team within a simulated commercial setting. Students will go through the key phases of software development from ideation through to development, testing, delivery, and publishing. Through the module students will learn how to manage and deliver commercial software development projects. This will include ethical, social and professional issues, project management, communication, time management, and team working strategies.
This module develops on the skills learnt in the first year and places them in a simulated commercial setting. The artefact produced as part of the software development process should be suitable for inclusion within a professional portfolio.
This module provides an opportunity for students in the School of Engineering and Physical Sciences to spend a year abroad at one of the University’s partner institutions. During the year abroad, students share classes with students at their chosen destination and study on a suite of locally delivered modules. This module will extend the length of your programme by one year and is taken between level 5 (year 2) and level 6 (year 3).
Inspired by the biological neurons that make up our brains, artificial neural networks (ANNs) are simple mathematical models that date back to the work of McCulloch and Pitts in the 1940s. Today, ANNs are the powerhouses of modern AI solutions and are regarded as one of the most important technical innovations of the past decade. In this module, we will follow the chronology of the deep learning revolution, starting with the basics of deep feed-forward networks and how to train them effectively. We will then work our way forwards in time and study convolutional neural network (CNN) architectures for images, recurrent neural networks (RNN) for time series data, and unsupervised models for representation learning. Towards the end of the module, students explore how deep nets learn and study the issues that can impact their real-world utility and the implications for society at large.
Digital image processing techniques are used in a wide variety of application areas such as computer vision, robotics, remote sensing, industrial inspection and medical imaging. Image processing is the study of algorithms that take images as an input and return information about these images. This module aims to provide a broad introduction to the field of image processing, culminating in a practical understanding of how to apply and combine techniques to various image-related applications. Students will have the opportunity to extract useful data from raw images and interpret the result.
The module introduces the fundamentals of machine learning and principled application of machine learning techniques to extract information and insights from data. The module covers supervised and unsupervised learning methods. The primary aim is to provide students with knowledge and applied skills in machine learning tools and techniques which can be used to solve real-world data science problems.
This module aims to equip you with the skills to design and develop connected, data-driven mobile applications, leveraging smartphone sensor technologies such as location, camera and proximity sensors. Consuming RESTful web services will be an area of focus for the data driven components of mobile app development. You can utilize contemporary tools to build mobile applications by applying industry-standard techniques for both code-base development and user-centered design.
This module offers students the chance to demonstrate their ability to work independently on a significant, in-depth project requiring the coherent and critical application of computer science theory and skills.
Students must initially produce a project proposal and related materials to frame the work, specifying clear, specific, academically justified, and appropriately scoped aims and objectives, as well as feasible means for fulfilling those aims and objectives. Students then work independently to fulfil those project goals. Throughout this process students are expected to demonstrate the application of practical development and analytical skills, innovation and/or creativity, and the synthesis of information, ideas and practices to generate a coherent problem solution.
The module aims to introduce the main concepts of Autonomous Mobile Robotics, providing an understanding of the range of processing components required to build physically embodied robotic systems, from basic control architectures to spatial navigation in real-world environments.
Students will have the opportunity to be introduced to relevant theoretical concepts around robotic sensing and control in the lectures, together with a practical “hands on” approach to robot programming in the workshops.
This module is intended to introduce students with the fast growing area of consumer electronics design.
Apart from interface and size issues, portable consumer electronics present some of the toughest design and engineering challenges in all of technology. This module breaks the complex design process down into its component parts, detailing every crucial issue from interface design to chip packaging, focusing upon the key design parameters of convenience, utility and size.
Realistic physics simulation is a key component for many modern technologies including computer games, video animation, medical imaging, robotics, etc. This wide range of applications benefiting from real-time physics simulation is a result of recent advances in developing new efficient simulation techniques and the common availability of powerful hardware.
The main application area considered in this module is computer games, but the taught content has much wider relevance and can be applied to other areas of Computer Science.
The field of software development is continuously evolving, driven in part by the increasing usage of generative AI and the requirement to protect applications from sophisticated cyber-attacks. This module aims to introduce students to modern development practices. Students will gain hands-on experience in tools and practices used for secure application development, deployment and monitoring.
This module aims to cover the theoretical fundamentals and practical applications of decision-making, problem-solving and learning abilities in software agents.
Search is introduced as a unifying framework for Artificial Intelligence (AI), followed by key topics including blind and informed search algorithms, planning and reasoning, both with certain and uncertain (e.g. probabilistic) knowledge. Practical exercises in AI programming will complement and apply the theoretical knowledge acquired to real-world problems.
This module aims to cover the theoretical fundamentals and practical application of machine learning algorithms, including supervised, unsupervised, reinforcement and evolutionary learning. Practical programming exercises complement and apply the theoretical knowledge acquired to real-world problems such as data mining.
The MComp Research Project involves both team-based and individual work that requires students to apply and integrate theoretical knowledge and practical skills from the breadth of their experience with computer science sub-disciplines. The form and nature of this project work is negotiable and provides opportunities to work in simulated commercial settings that are scenario based or primarily focused on a specific research domain. Students will work towards developing a significant software artefact that supports the research area of interest and present the outcomes of their work through written and presentation components.
This module will explore current methodologies in the field of signal and image processing, covering a range of aspects in capturing, processing, analysing and interpreting n-dimensional content.
The aim is to offer students with a deep understanding and to allow an exposure to the latest developments in signal and image processing, equipping them with knowledge in practical depth. The module will also provide training in programming skills (e.g. Matlab), tools and methods that are necessary for the implementation of such systems.
The module will also cover applications of signal and image processing in various fields, allowing the students the chance to establish a full awareness of technology advances in this rapidly evolving field.
This module explores current methodologies in the field of big data analytics and modelling, covering a range of aspects in collecting, transforming, processing, analysing and make inferences out of large amounts of data, which can either be signals or visual data.
The aim is to offer students a deeper understanding and to allow an exposure to the latest developments in big data analytics, equipping them with knowledge in practical depth. The module will also provide training in programming skills (e.g. python), tools and methods (e.g. Apache Spark, Spark Machine/Deep Learning, distributed analytics, etc.) that are necessary for the implementation of big data analytics systems.
The module will also cover applications of big data analytics in various fields, such as Cybersecurity, Internet of Things, and Computer Vision, allowing students the chance to establish a full awareness to the technology advance in this rapidly evolving field.
This module aims to explore current methodologies in the field of computer vision, covering a range of aspects in capturing, processing, analysing and interpreting rich visual content.
The aim is to offer students with a deep understanding and to allow an exposure to the latest developments in computer vision, equipping them with knowledge in practical depth. The module will also provide the opportunity for training in programming skills (e.g. Matlab), tools and methods that are necessary for the implementation of computer vision systems.
The module will also cover applications of computer vision in various fields, such as in object recognition/tracking, medical image analysis, multimedia indexing and retrieval and intelligent surveillance systems, allowing the students the opportunity to establish a full awareness to the technology advance in this rapidly evolving field.
The module introduces the fundamentals of neural computing, an emergent specialised area of computer science that is concerned to describe how the brain “computes” by simplifying neuronal biology to a set of equations.
Emphasis will be given on mathematical descriptions and computational techniques used to study and understand neurons and network of neurons. Specific topics will cover synaptic transmission and plasticity, learning and memory and vision processing including applications in object recognition and scene understanding.
Students can develop an understanding of core neural computing concepts and models, the current vision technology landscape, and topical application scenarios using a number of computational tools.
† Some courses may offer optional modules. The availability of optional modules may vary from year to year and will be subject to minimum student numbers being achieved. This means that the availability of specific optional modules cannot be guaranteed. Optional module selection may also be affected by staff availability.
Starting university is a big step, especially in a technical subject where you may be learning programming, software engineering, and AI concepts at the same time. Lincoln’s teaching approach is designed to help you build confidence as you progress.
You'll learn through:
The programme's modular structure typically consists of 12 weeks per module, combining lectures with workshops for hands-on practical experience. You'll also be expected to build your learning through self-directed study outside formal teaching.
Support may include:
You do not need to arrive as an expert programmer or AI specialist. The course is designed to help you develop from core computing foundations into more advanced AI and master's-level study.
Gain hands-on experience in a real workplace and apply your learned skills in a professional setting.
You'll pay the relevant placement year fee if you choose a placement year and cover your own travel and living costs.
Artificial intelligence and computer science skills are increasingly valuable across many sectors. Organisations need graduates who can build software, work with data, understand intelligent systems, and solve technical problems.
This degree can support career paths in areas such as:
Possible graduate roles include:
You can graduate with:
The MComp route may also support progression into specialist technical roles, postgraduate research, or further study in areas such as artificial intelligence, data science, robotics, cybersecurity, and computer science
112 to 120 UCAS Tariff points.
This must be achieved from a minimum of 2 A Levels or equivalent Level 3 qualifications. For example:
A Level: BBC to BBB
BTEC Extended Diploma: Distinction, Distinction, Merit.
T Level: Merit Overall
Access to Higher Education Diploma: 112 to 120 UCAS points to be achieved from 45 Level 3 credits.
International Baccalaureate: 30 points overall.
GCSEs: Minimum of three at grade 4 or above, which must include English. Equivalent Level 2 qualifications may be considered.
The University accepts a wide range of qualifications as the basis for entry and do accept a combination of qualifications which may include A Levels, BTECs, EPQ etc.
We may also consider applicants with extensive and relevant work experience and will give special individual consideration to those who do not meet the standard entry qualifications.
Non UK Qualifications:
If you have studied outside of the UK, and are unsure whether your qualification meets the above requirements, please visit our country pages for information on equivalent qualifications.
/studywithus/internationalstudents/entryrequirementsandyourcountry/
International students will be required to demonstrate English language proficiency equivalent to IELTS 6.0 overall, with a minimum of 5.5 in each element. For information regarding other English language qualifications we accept, please visit the English Requirements page.
If you do not meet the above IELTS requirements, you may be able to take part in one of our Pre-sessional English and Academic Study Skills courses.
The ÇéÉ«ÁùÔÂÌì's International College also offers university preparation courses for international students who do not meet the direct entry requirements. Upon successful completion, students can progress to Bachelor's study at the ÇéÉ«ÁùÔÂÌì. Please visit/internationalcollege/ for more information.
For applicants who do not meet our standard entry requirements, our Science Foundation Year can provide an alternative route of entry onto our full degree programmes:
If you would like further information about entry requirements, or would like to discuss whether the qualifications you are currently studying are acceptable, please contact the Admissions team on 01522 886097, or email admissions@lincoln.ac.uk.
This course could be a good fit if you:
You do not need to know exactly which career path you want to follow yet. This degree helps you build broad computing and AI skills that can open doors across multiple industries.
University Study is a major investment, so it’s important to understand the costs and support available. A full breakdown of the fees associated with this programme can be found below. Eligible students may be able to access scholarships and bursaries to help with study costs.
The best way to find out what it is really like to live and learn at Lincoln is to visit us in person. We offer a range of opportunities across the year to help you to get a real feel for what it might be like to study here.
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We want you to have all the information you need to make an informed decision on where and what you want to study. In addition to the information provided on this course page, our What You Need to Know page offers explanations on key topics including programme validation/revalidation, additional costs, and contact hours.