Curious about studying MSc Artificial Intelligence and its Applicationsat 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.
MSc - Master of Science
University of Essex
Full Time
Oct 2026
1 Year
Artificial intelligence (AI) is a broad area of research that uses rigorous and hands-on computational techniques to address significant problems that arise in many different disciplines, including, but not limited to, computing systems, economics, business, healthcare, games, medicine design, education and agriculture. With the rapid advance of AI, there is a growing demand for students who can understand and promote AI to all these sectors.Our MSc Artificial Intelligence and its Applications conversion course will equip you with a skillset on AI techniques, and prepare you for meeting demand, regardless of your academic background. You will obtain a comprehensive perspective of the current developments, the needs and the potential of AI, by participating in taught modules that promote a deep understanding of AI methods together with others that deliver the necessary technical expertise.This course is developed in collaboration with industry partners and public sector organisations, which include BT, Profusion, Essex County Council, Essex Police, and Suffolk County Council. Our active links with industry can broaden your employment potential and offer placement opportunities.With a skillset that combines both technical and analytical know-how from our School of Computer Science and Electronic Engineering, our graduates will be able to find employment in very competitive institutions in a wide range of disciplines. A career in artificial intelligence can be realised within a wide range of sectors, including health, finance, education etc, both in the public and the private sector. Examples of specific jobs held by our AI graduates include computer/data/research scientists, software developers and analysts.
What fascinates you? Apply your learning in computer science or engineering to solve a problem. Design, implement and evaluate a solution, producing a dissertation on your investigation and giving an oral presentation of your work. Test your knowledge, while gaining practical experience and building your project management skills.
Search engines have become the first entry point into a world of knowledge and they form an essential part of many modern computer applications. While much of the underlying principles have been developed over decades, the landscape of search engine technology has changed dramatically in recent years to deal with data sources magnitudes larger than ever before (the rise of 'big data'). As a result of this, new paradigms for storing, indexing and accessing information have emerged. This module will provide the essential foundation of information retrieval and equip students with solid, applicable knowledge of state-of-the-art search technology.
In this module you will be introduced to the necessary mathematical foundations and tools for working with data. You will become familiar with standard mathematical notation and concepts, and with common techniques such as differentiation, integration, metrics, vectors and matrices.
Teamwork skills are essential for employability. The aim of this module is to provide students with the opportunity to apply their specialised knowledge to a realistic problem and gain practical experience of the processes involved in the team-based production of software. Wherever possible, teams are organised on the basis of shared interest, and the problem is designed to exercise their understanding of their area of specialised study. Starting from an outline description of a realistic problem, each team is required to develop a fully implemented software solution using appropriate engineering and project management techniques.
This module introduces Artificial Intelligence (AI), the science of making computers and machines display intelligent behaviour. This multidisciplinary activity draws from computer sciences, mathematics and statistics, and also elements of philosophy, logic and even psychology. Today, AI is ubiquitous in society, from self-driving cars to spam filters and finance trading to video games. The increasing dependence on AI will reshape society and economy. Understanding AI principles, applications, and limitations is important for all students, regardless of their background, and this module assumes no prior knowledge. This module provides both theoretical and practical techniques, covering AI theory and fundamentals of machine learning models, as well as their implementation and applications.
This module aims to prepare students for conducting an independent research project leading to a dissertation and to provide them with an appreciation of research and business skills related to their professional career. As a precursor to their project students, individually select an area of Computer Science, or Electronic Engineering, or Computational Finance and perform the necessary background research to define a topic and prepare a project proposal under the guidance of a supervisor. The module guides them by a) introducing common research methods b) creating an understanding of basic statistics for describing and making conclusions from data c) helping to write a strong proposal including learning how to perform literature search and evaluation and d) giving an in-depth view into the business enterprise, financial and management accounting and investment appraisal.
The module will introduce you to concepts from data analysis and statistics and show how they can be applied effectively via the R language. It will cover a wide introduction to statistics and provide practical experience of real-world examples of how statistics is used to gain insights. Throughout these examples, and many more, we will teach programming techniques that will enable you to apply statistical approaches to real-world applications. This module assumes no previous exposure to statistics.
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