Loughborough University
Leicestershire, UK
LE11 3TU
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Loughborough University

Programme Specifications

Programme Specification

MSc Artificial Intelligence

Academic Year: 2020/21

This specification provides a concise summary of the main features of the programme and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if full advantage is taken of the learning opportunities that are provided.

This specification applies to delivery of the programme in the Academic Year indicated above. Prospective students reviewing this information for a later year of study should be aware that these details are subject to change as outlined in our Terms and Conditions of Study.

This specification should be read in conjunction with:

  • Summary
  • Aims
  • Learning outcomes
  • Structure
  • Progression & weighting

Programme summary

Awarding body/institution Loughborough University
Teaching institution (if different)
Owning school/department Department of Computer Science
Details of accreditation by a professional/statutory body


Final award MSc (PGDip and PGCert as exit awards only)
Programme title Artificial Intelligence
Programme code COPT17
Length of programme One year (full time)
UCAS code N/A#
Admissions criteria
Date at which the programme specification was published Wed, 09 Sep 2020 13:19:37 BST

1. Programme Aims

  • To inspire students to have interest and enthusiasm for artificial intelligence (AI), and to involve them in an intellectually stimulating and satisfying experience of learning and studying. 
  • To give students the knowledge and skills needed to become effective AI specialist professionals within the computing industries. 
  • To provide students with specialised areas of study so that they can understand and apply the theory and practice of AI. 
  • To provide students with the skills to undertake research projects in AI. 
  • To develop, through a variety of educational activities, a range of transferable skills applicable to employment.

2. Relevant subject benchmark statements and other external reference points used to inform programme outcomes:

  • QAA Computing Benchmark
  • The National Framework for Higher Education Qualifications

3. Programme Learning Outcomes

3.1 Knowledge and Understanding

On successful completion of this programme, students should be able to demonstrate comprehensive knowledge and understanding of: 

K1 Theory and programming methods for the construction of AI systems

K2 AI methods and algorithms

K3 Machine learning and its application to different problems

K4 Data mining and handling large data sets

K5 Image processing and coding

K6 Robotics, control and intelligent/autonomous systems

3.2 Skills and other attributes

a. Subject-specific cognitive skills:

On successful completion of this programme, students should be able to:

C1 Analyse requirements for, model and design computer-based systems in AI

C2 Critically evaluate different machine learning approaches; visual systems and associated technology; and different algorithms in data mining

C3 Analyse commercial and scientific risk associated with a computing project in their specialist area

b. Subject-specific practical skills:

On successful completion of this programme, students should be able to:

P1 Build, implement and test complex computer-based AI systems that are well structured, reliable and useable and take into consideration context and appropriate visual interfaces

P2 Develop practical machine learning models for solving real-world problems

P3 Deploy tools and computer equipment for the implementation and documentation of computer-based AI systems

P4 Implement learning and control algorithms for robotics applications using advanced AI techniques

P5 Apply research methodologies in a computing context to produce novel and leading-edge outcomes

P6 Design a control system that applies to robotics and evaluate its risks and performance

P7 Plan and manage a research project in computer-based systems

c. Key transferable skills:

On successful completion of this programme, students should be able to:

T1 Employ research and information-retrieval skills

T2 Apply calculations to solve cases involving a quantitative dimension

T3 Manage their own learning and development, including time management and organisational skills

T4 Plan and manage a project to complete within schedule and resource availability

T5 Present their work in the form of reports, presentations or demonstrations

4. Programme structure

Semester 1

Compulsory modules (60 credits) 





Programming for Specialist Applications



Artificial Intelligence



Robotics and Intelligent Systems



Machine Learning


Semester 2

Compulsory modules (60 credits) 





Research Methods



Data Mining



Computer Vision



Project Preparation


Semester 3

Compulsory module (60 credits) 





AI Project



5. Criteria for Progression and Degree Award

In order to be eligible for the award, candidates must satisfy the requirements of Regulation XXI.

6. Relative Weighting of Parts of the Programme for the Purposes of Final Degree Classification


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