MSc Artificial Intelligence

How You Study

Students on this programme can experience a blend of different teaching and learning approaches. The programme aims to enable the development of skills through practical workshops in the laboratory, and academic knowledge through debate, lectures, discussion, and personal research.

Modules assume a familiarity with programming concepts and the supporting mathematical framework, while presenting advanced concepts relating specifically to the computing domain.

Each module typically consists of 12 weeks of study. This time includes a supporting lecture programme, a series of supported laboratory sessions, and time for the completion of assignment exercises and examinations. Weekly contact hours on this programme may vary depending on the individual module options chosen and the stage of study.

The programme is also supported by online access to lecture material and related information.

Postgraduate level study involves a significant proportion of independent study, exploring the material covered in lectures and seminars. As a general guide, for every hour spent in class, students are expected to spend at least two to three hours in independent study.

Students must complete a substantial research project focusing on an area of personal and professional interest, for example through a substantive software implementation and the development of a dissertation.

The October 2022 intake will take the following structure:

Term A: Applied Signal and Image Processing (Core), Advanced Artificial Intelligence (Core), and Neural Computing (Core).

Term B: Computer Vision (Core), Frontiers of Machine Learning and Computer Vision Research (Core), Machine Learning (Core), and Big Data Analytics and Modelling (Core).

Term C: Research Methods (Core) and Research Project (Core).

Advanced Artificial Intelligence (Core)

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.

Advanced Machine Learning (Core)

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.

Applied Signal and Image Processing (Core)

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.

Big Data Analytics and Modelling (Core)

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.

Computer Vision (Core)

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.

Frontiers of Machine Learning and Computer Vision Research (Core)

This module is designed to give students the opportunity to develop an understanding of the state of the art in machine learning and computer vision research, including an understanding of the theoretical developments and current applications in the field.

Neural Computing (Core)

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.

Research Methods (MSc Computer Science) (Core)

This module is designed to cover the fundamental skills and background knowledge that students need to undertake research related to the title of the award being studied, including: surveying literature; selecting and justifying a research topic; planning of research; selection of appropriate research methods; evaluation of research; presentation and reporting of research; and legal, social, ethical and professional considerations.

Research Project (Core)

This module gives students with the opportunity to carry out a significant project, focusing on an area of particular personal and professional interest, through the development of a dissertation and substantive software implementation.

The research project is an individual piece of work, which gives students the chance to apply and integrate elements of study from a range of modules, centred on a specific research question. Students are expected to undertake work that is relevant to the ongoing research in one of the established research centres within the Lincoln School of Computer Science and will work closely under the supervision of a member of that research centre.

Students are required to undertake the development of a software artefact that is non-trivial in scale and goals, and is supported by best-practice application of appropriate theoretical frameworks.

How You Are Assessed

The programme may be assessed through a variety of means, including in-class tests, coursework, projects, and examinations. The final stage research project provides further opportunity to specialise and to complete an extended piece of work.

The University of Lincoln's policy on assessment feedback aims to ensure that academics will return in-course assessments to you promptly usually within 15 working days after the submission date.

How You Study

Students on this Program can experience a blend of different teaching and learning approaches. The Program aims to enable the development of skills through practical workshops in the laboratory, and academic knowledge through debate, lectures, discussion, and personal research.

Modules assume a familiarity with programming concepts and the supporting mathematical framework, while presenting advanced concepts relating specifically to the computing domain. Each module typically consists of 12 weeks of study. This time includes a supporting lecture Program, a series of supported laboratory sessions, and time for the completion of assignment exercises and examinations. Weekly contact hours on this Program may vary depending on the individual module options chosen and the stage of study. The Program is also supported by online access to lecture material and related information.

Postgraduate level study involves a significant proportion of independent study, exploring the material covered in lectures and seminars. As a general guide, for every hour spent in class, students are expected to spend at least two to three hours in independent study.

Students must complete a substantial research project focusing on an area of personal and professional interest, for example through a substantive software implementation and the development of a dissertation.

This programme aims to provide students with skills spanning two key disciplines of modern computing and its applications, namely imaging and data science, and their combined use. Such skills are in high demand not only in academia and industries dealing with imaging technologies and related challenges, but also in many other areas where analytical and multidisciplinary mindsets and skills are critical. Some students may choose to continue towards doctoral level, including within the School of Computer Science.