KV5032 - AI Methods

What will I learn on this module?

This module will give you an overview of typical artificial intelligence (AI) methods. You will learn about the philosophy and underpinning concepts of AI, such as problem solving by searching, knowledge representation and reasoning, and machine learning. You will develop a good understanding around the user, professional, ethical, social, legal, and economic issues and risks surrounding the design, development, operation and maintenance of AI systems.

‘AI Methods’ will prepare you for late modules such as Machine Learning, and can be one of the key providers for the technologies required by Computing Placement in your third year and Computing Dissertation in your final year. Industrial case studies will be integrated in the module to demonstrate potential real-world applications, and its role as an enabler for many societal and environmental challenges, such as EDI and green computing.

During ‘AI methods’ you will walk through a series of typical AI approaches, making use of Northumbria’s state-of-the-art computer labs. You will also engage with several simplified industry-relevant cases as part of your research-rich learning. The assessment consists of a literature review of a sub-field of AI and a product that applies AI algorithms in your chosen sub-field to computing problems.

How will I learn on this module?

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.

How will I be supported academically on this module?

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.

What will I be expected to read on this module?

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

What will I be expected to achieve?

Knowledge & Understanding:
MLO1 – Develop knowledge and understanding of typical AI algorithms and their applications
MLO2 – Select and apply AI algorithms to a directly relevant computing problem and evaluate your solution
MLO3 – Demonstrate 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 –evaluate AI applications and their societal and environmental implications

How will I be assessed?

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 all MLOs. 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.





Module abstract

This module introduces you to the principles, underpinning 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.

Course info

UCAS Code G407

Credits 20

Level of Study Undergraduate

Mode of Study 3 years full-time or 4 years with a placement (sandwich)/study abroad

Department Computer and Information Sciences

Location City Campus, Northumbria University

City Newcastle

Start September 2024 or September 2025

Fee Information

Module Information

All information is accurate at the time of sharing. 

Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.  

Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.


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