KV7004 - AI Studio

This module will be delivered using lectures and tutorials. Traditional lectures will impart to you an inclusive high level understanding of artificial intelligence and its technologies, to help you to develop a comprehensive understanding of the technological background to the subject matter. You will learn through a combination of learning and teaching methods including lectures, tutorials, and directed independent learning. You will learn about concepts, mathematical principles and theories in formal lectures and these will be demonstrated, practised and discussed in tutorial sessions. Lectures will offer you the opportunity to develop your knowledge of the principles and concepts of different approaches in artificial intelligence. The tutorials will be used to reinforce your learning from the lectures through the use of examples and case studies which you will research, discuss and receive feedback from the tutors. Tutorials will enable you to develop your ability to think independently and make judgements on matters relating to artificial intelligence and other machine learning techniques. During tutorial sessions, you will complete/write program code and analyse/solve machine learning problems. Two guided independent learning homework exercises will also be set for you to work on outside of class time. These will include problems in artificial intelligence intended to aid your understanding. The rationale for the problem solving tasks during seminars is to support your learning by addressing or finding solutions for real-world scenarios. The aim is to broaden and deepen your understanding of the theoretical and practical aspects of the subject, which in turn will help broaden your professional skills and abilities.


You will have electronic assess, via the university’s e-learning portal and online library resources, to all learning materials, relevant text books and seminar materials.

You will be expected to undertake directed and independent learning to support your development.

Academic support for this module will be provided by tutors in a variety of ways to suit your individual needs.

The tutorial sessions will encourage you to identify and explore unconsolidated areas of learning to support your progression through the module. Coursework feedback will be provided via the module electronic portal site addressing generic consideration of your work. Individual feedback will be provided to coursework submissions to clarify points of learning that have not been fully assimilated. You will receive marked coursework with written feedback on Blackboard.

Resources from lecturers and tutorials will be uploaded onto the University’s Blackboard site. The reading list for the module is held online and will be accessible via the University’s Blackboard site. The list will allow you online access to the key resources on the subject.

All modules at Northumbria include a range of reading materials that students are expected to engage with. Online reading lists (provided after enrolment) give you access to your reading material for your modules. The Library works in partnership with your module tutors to ensure you have access to the material that you need.

Knowledge & Understanding:

1. Demonstrate knowledge and critical understanding of the core artificial intelligence concepts, tools and technologies used in building intelligent systems for solving practical problems
2. Demonstrate appreciation of the artificial intelligence’s contribution to the scientific community
3. Articulate a broad awareness of the ethical aspects of artificial intelligence

Intellectual / Professional skills & abilities:

4. Ability to evaluate available artificial intelligence techniques, tools and technologies and assess their applicability in novel domains
5. Critically analyse and evaluate the effectiveness and efficiency of intelligent systems


Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):

6. Developing and reinforcing the ethical characteristics of a Northumbria graduate as you consider the values that underpin ethical approaches to applying artificial intelligence and its technologies (the students will have the opportunity to reflect on how these are linked to their own values).

Summative assessment
Summative assessment, which your mark module mark will be based on, consists of two assignments:

1. Assignment 1(50%): You will write a research report on innovative and emerging AI technologies and applications (2000 words). This assignment will assess MLOs 1, 2, and 4. Students will be given written feedback on the assignment which they can feed forward into future assessment in this subject area

2. Assignment 2 (50%): You will conduct performance evaluation of an existing AI application / tool and present your evaluation results in a report (maximum 2000 words). This assignment will assess MLOs 1, 3, 5, and 6.

You will be provided with written, electronic, feedback on the assignments.

Formative assessment and feedback
You will receive guidance and ongoing feedback on your work and progress verbally in lecture and workshop sessions.

You will also have the opportunity to discuss your progress, complete practicals and the needs of the summative assessment informally in the tutorial sessions.

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This module will provide a general introduction to the subject matter relating to the artificial intelligence and machine learning. It will cover fundamental ideas in problem formulation and solving, knowledge representation and machine learning techniques. It will provide students with the appropriate background that is necessary to undertake further investigation into advanced and specialized artificial intelligence and machine learning modules. You will learn key theoretical concepts, state-of-the-art techniques and research advances in the field of intelligent systems. Upon completing the module, you will have a holistic understanding of how artificial intelligence works in principle and in applications. In particular, you will have a high-level understanding of the artificial intelligence and machine learning techniques coupled with hands-on introductory programming. The module will use case studies to support the tutorials that are developed from research and industrial projects. There will be opportunity throughout the delivery of the module to attend tutorial where you can discuss and practice designing solutions and gain feedback from your module tutors.