| Unit name | Foundations of Artificial Intelligence |
|---|---|
| Unit code | SEMTM0051 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Aitchison |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Science and Engineering |
Why is this unit important?
Artificial Intelligence is a critical emerging area that promises to support the solution of many technological and societal problems. The aim of this unit is to provide a broad technical foundation in the field of Artificial Intelligence (AI), introducing core concepts, methodologies, and algorithms from across the discipline, including machine learning, optimisation, and artificial neural networks. There is an emphasis on applications across a wide range of industrial and scientific applications, and on their mathematical foundations.
How does this unit fit into your programme of study
Foundations of AI is a core unit in which the methods of modern artificial intelligence are explored in detail. You will build a solid practical understanding of mathematical and computational fundamentals of AI, including modern algorithms and techniques. This will then form the foundation for taking more advanced courses on AI in TB2
An overview of content
This course will cover modern approaches to Artificial Intelligence (AI) with a focus on artificial neural networks. The unit will cover the formalisation of problems commonly addressed by AI, an introduction to machine learning methodologies and algorithms, and a discussion of neural network architectures, loss functions, optimisation, and backpropagation. The unit will provide you with the necessary knowledge and context to understand and adequately apply different Artificial Intelligence methodologies.
How will students, personally, be different as a result of the unit
Throughout the unit there is a focus on students understanding theory and computational modelling principles in order to apply them effectively to AI. At the end of the unit, students will be able to apply these principles to a range of practical AI tasks.
Learning outcomes
On successful completion of this unit, you will be able to:
You will learn through a range of synchronous and asynchronous activities including lectures and problem classes/computer laboratories. Problem classes/computer laboratories will be used to provide direct feedback on linked exercises.
Tasks which help you learn and prepare you for summative tasks (formative):
The lecture notes will provide questions and worked answers. Additionally, some of the practical lab sheets will provide exercises and worked answers. Feedback on these questions and exercises will be provided by a combination of laboratory/problem classes and Q&A sessions.
Tasks which count towards your unit mark (summative):
In-person exam (70%) assessing all Learning Outcomes. This is a closed-book individual exam assessing the student’s mastery of the theoretical and conceptual aspects of artificial intelligence methods and technologies.
Programming-based coursework (30%) assessing ILOs 3 and 4.`This is an implementation-based coursework consisting of programming assignments, in which students will demonstrate their practical understanding and implementation skills to deploy AI systems to solve prediction problems based on public datasets.
When assessment does not go to plan:
When required by the Board of Examiners, you will normally complete reassessments in the same formats as those outlined above. However, the Board may modify the form or number of reassessments required. Details of reassessments are normally confirmed by the School shortly after the notification of your results at the end of the academic year.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. SEMTM0051).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
