KV5032 - AI Methods

You will learn through lectures, workshops, and independent learning. The lectures will cover theories and concepts of typical AI algorithms that will enable you to code these AI algorithms in a series of guided exercises. You will work on these during workshops and hands-on sessions usually in Northumbria’s CIS building computer labs.

You will be supported by lecturers during the timetabled sessions when you will receive feedback on your work. The University’s eLearning Portal offers remote access to all lecture and seminar materials to reinforce your learning. In addition, the university library offers support for all students through providing electronic resources.

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:
MLO1 – Develop knowledge and understanding of typical AI algorithms, the underpinning math, statistics and natural science theories, and their applications
MLO2 – Select and apply AI algorithms to a directly relevant computing problem and critically evaluate your solution
MLO3 – Demonstrate a critical understanding of user, professional, ethical, social, legal, and economic issues and risks surrounding the design, development, operation, and maintenance of AI systems

Intellectual / Professional skills & abilities:
MLO4 - Develop critical computational thinking and its relevance to automation and everyday life


Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
MLO5 – Critically evaluate AI applications and their societal and environmental implications

The summative assessment will consist of two assessments. The first assessment will be a critical literature review of a sub-filed of AI and its societal and environmental implications. The word limit for this assessment is 1500 words. This will be worth 40% of the marks available and will address MLO1 and MLO5. The literature review will include an overview of the typical algorithms in the sub-field and their applications. You will receive both informative and confirmatory feedback which will be expected to support the developed of your second assessment.
The second assessment will require you to develop an application of AI algorithms in the subfield of AI that you have chosen for your assessment 1. You will also report your solution and discuss associated considerations around user, professional, ethical, social legal and economic issues and risks of your solution. The word limit for the report is 1000 words. This will be worth 60% of the marks available and will address MLO 1-4. You will receive written feedback on your work.
On an on-going basis you will also receive formative feedback on exercises you are required to complete.

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This module introduces you to the principles, underpinning theories and concepts, and typical implementations of AI methods. Indicative topics include knowledge representation and reasoning, search (optimisation), and machine learning. Theory is followed by hands-on workshops in our state-of-the-art computer labs where you will be walked through typical implementations of AI algorithms using simplified real-world applications that the module tutors have worked upon as knowledge transfer projects. In addition to a holistic overview of the AI methods, you will be assessed by bringing together all your new skills and techniques in building a substantial project where the associated societal, environmental, professional, ethical, social, legal, and economic issues will also be considered. Formative feedback will be provided to help you shape your project and support its potential future commercial exploitation.