Curious about studying Artificial Intelligence - BSc (Hons)at University of Essex? We've gathered all the key details—entry requirements, modules, fees, and more. Hear from real students by checking out reviews, or take the next step by booking an open day to explore it for yourself.
Bachelor of Science (with Honours) - BSc (Hons)
University of Essex
Full Time
Oct 2026
3 Year
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What is the BSc (Hons) Artificial Intelligence?The BSc (Hons) Artificial Intelligence is a three-year degree, extendable to four with an optional year in placement or abroad.
You’ll gain expertise that is in huge demand within the UK industry with a strong understanding of theoretical and practical aspects of AI. You’ll also build a strong foundation in core computer science theory and explore its cutting-edge real-world applications, preparing you to create the technologies of tomorrow. ###
Why this courseThe BSc (Hons) Artificial Intelligence gives you a comprehensive perspective of the current developments, needs and potential of AI models, algorithms and systems. You’ll study a broad curriculum shaped by industry partners, gaining the most up-to-date understanding of computer science. Discover which areas you’re interested in exploring further such as assistive technologies, computer games, Big Data and robotics. Add a placement year or study abroad to put your learning into practice, expand your perspective and build confidence in professional or international settings. By graduation, you’ll have the confidence and ability to apply AI to a range of disciplines and sectors. You’ll be ready for careers in software and systems analysis, engineering and programming. ###
Who should apply* Future programmers and engineers * Those wanting to use AI applications to revolutionise people’s lives * Students eager to develop their own computer games, apps, online platforms and digital systems * Computational thinkers with great grasp of pattern recognition and algorithms
Our Team Project Challenge gives you the opportunity to develop a range of professional skills by working as part of a small student team on a specific project. The projects are research-based and incorporate the concepts of specifications, design, and implementation. You’ll learn about sustainability, project and time management, design, legal issues, health and safety, data analysis and presentation, team reporting, and self-evaluation. You’ll also develop skills such as critical thinking and problem solving, agility, leadership, collaboration across networks, and effective oral and written communication, as well as curiosity and imagination, all of which will enhance your knowledge, confidence and social skills necessary to innovate and succeed in a competitive global environment.
The aim of this module is to cover fundamental mathematics for Computer Scientists. It does not assume A-level mathematics, and the emphasis and delivery will be on understanding the key concepts as they apply to Computer Science.
The aim of this module is to provide an introduction to the fundamental concepts of computer programming. After completing this module, students will be expected to be able to demonstrate an understanding of the basic principles and concepts that underlie the procedural programming model, explain and make use of high-level programming language features that support control, data and procedural abstraction. Also, they will be able to analyse and explain the behaviour of simple programs that incorporate standard control structures, parameterised functions, arrays, structures and I/O.
Databases are everywhere. They are employed in banking, production control and the stock market, as well as in scientific and engineering applications. For example, the Human Genome Project had the goal of mapping the sequence of chemical base pairs which make up human DNA. The result is a genome database. This module introduces the underlying principles of databases, database design and database systems. It covers the fundamental concepts of databases, and prepares the student for their use in commerce, science and engineering.
Computers, embedded systems, and digital systems in general have become an essential part of most people's lives, whether directly or indirectly. The aim of this module is to introduce the software and hardware underpinnings of such systems at an introductory yet challenging level suitable for future computer scientists and engineers. Topics covered in the module include both top-view as well as bottom-view approaches to understanding digital computers. They range from the more theoretical (e.g., state machines, logic circuits, and von Neumann's architecture) to the more practical (e.g., how transistors produce binary signals, operating system functions, memory management, and common hardware devices). The module also includes problem solving classes in which a guided discussion of weekly exercises is aimed at giving the student an opportunity to consolidate his/her understanding of the topics involved. Upon completion of this module, students should have a good conceptual and practical understanding of the nature and architecture of digital computer systems and their components.
Want to become a Java programmer? Topics covered in this module include control structures, classes, objects, inheritance, polymorphism, interfaces, file I/O, event handling, graphical components, and more. You will develop your programming skills in supervised lab sessions where help will be at hand should you require it.
The aim of this module is to provide students with an introduction to the principles and technology that underlie internet applications and the techniques used in the design and construction of web sites. Students showcase their skills by designing and building both client and server components of a data driven web site.
Data structures and algorithms are two of the foundations of computer science, distinguishing it from just programming. In this module, you will learn what algorithms are and how they can be implemented even in very simple computers. You will cover fundamental algorithms for sorting and searching, along with data structures for lists and trees, and will be introduced to the analysis of algorithm efficiency.
This course covers the principles of project management, team working, communication, legal issues, finance, and company organisation. Working in small teams, students will go through the full project life-cycle of design, development and implementation, for a bespoke software requirement. In this course, students gain vital experience to enable them to enter the computer science/Electrical engineering workforce, with a degree backed by the British Computer Society, and by the Institute of Engineering and Technology.
This module aims to equip students with the main principles guiding the activities involved in software development throughout its lifecycle, including software requirements, object-oriented analysis and design, software validation and testing, and software maintenance and software evolution.
This module extends the students' knowledge and skills in object-oriented application programming by a treatment of further Java language principles and of important Application Programming Interfaces (APIs). The Java Collections API is explored in some more detail with emphasis on how to utilise these classes to best effect. A particular focus will be on the interaction with databases (e.g. via JDBC) and on writing secure applications.
Data structures and algorithms lie at the heart of Computer Science as they are the basis for the 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 computability.
Artificial intelligence will be a great driver of change in the coming decades. This module provides an introduction to three fundamental areas of artificial intelligence: search, knowledge representation, and machine learning. These underpin all more advanced areas of artificial intelligence and are of central importance to related fields such as computer games and robotics. Within each area, a range of methodologies and techniques are presented, with emphasis being placed on understanding their strengths and weaknesses and hence on assessing which is most suited to a particular task.
This module is designed to provide an introduction to the statistical principles used in data science and their applications, and the use of practical programming packages for data analysis and visualisation. You will also study data analysis techniques, including causal inference, correlation, classification, regression, and clustering.
The highlight of our undergraduate degree courses is the individual capstone project. This project module provides students with the opportunity to bring together all the skills they have gained during their degree and demonstrate that they can develop a product from the starting point of a single 1/2 page description, provided either by an academic member of staff or an external company. In all the student spends 450 hours throughout the academic year, reporting to their academic tutor, and in the case of company projects, to a company mentor. All projects are demonstrated to external companies on our Project Open Day.
As humans we are adept in understanding the meaning of texts and conversations. We can also perform tasks such as summarize a set of documents to focus on key information, answer questions based on a text, and when bilingual, translate a text from one language into fluent text in another language. Natural Language Engineering (NLE) aims to create computer programs that perform language tasks with similar proficiency. This course provides a strong foundation to understand the fundamental problems in NLE and also equips students with the practical skills to build small-scale NLE systems. Students are introduced to three core ideas of NLE: a) gaining an understanding the core elements of language--- the structure and grammar of words, sentences and full documents, and how NLE problems are related to defining and learning such structures, b) identify the computational complexity that naturally exists in language tasks and the unique problems that humans easily solve but are incredibly hard for computers to do, and c) gain expertise in developing intelligent computing techniques which can overcome these challenges.
This module provides an introduction to machine learning techniques, ranging from supervised learning, unsupervised learning, and deep learning. Also included are the evaluation metrics and procedures of various learning methods. The module focuses on classification and regression learning tasks and their applications to real-world problems.
Computer vision is the discipline that tries to understand the content of images and videos. It has an extraordinarily wide range of applications; well-known ones include inspection on production lines, reading number plates, mixing live and computer-generated action in movies, and recognising faces. However, researchers are working on applications such as driverless cars, building 3D models from photographs, robot navigation, gaming interfaces, and automated medical diagnosis -- in fact, whenever you as a human looks at the world and try to understand what you see is fair game for computer vision. This module introduces you to the principles of computer vision through a series of lectures and demonstrations. You have an opportunity to learn how to use these principles and algorithms on real-world vision problems in the associated laboratories using the industry-standard toolkit, OpenCV.
112-120 Grades/points required
Not currently available, please Contact University for up to date information.
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