| Unit name | Applications of Artificial Intelligence in Finance and Health |
|---|---|
| Unit code | SEMT30011 |
| Credit points | 20 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Professor. Lawry |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Methods of AI, Problem Solving with AI or equivalent |
| 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 Engineering |
Why is this unit important?
This unit provides the opportunity to explore in detail how AI is applied to two key application domains; Health and Finance. It will consider the types of problems that AI is applied to and the kind of AI tools and algorithms that are useful in each case. For each domain, the unit will discuss the ethical and regulatory issues that occur and consider the importance of transparency and explainability. It will also look at any similarities between the application domains and identify generic application principles that are relevant to both.
How does this unit fit into your programme of study
This unit enables students to bring together the methods and algorithms that they have learnt and apply them at scale to real world applications in finance and health. The unit will provide students with detailed background on these domains so that they can bring their problem-solving skills to bear on important societal and economic challenges that require mature consideration of how AI should be applied fairly and ethically.
An overview of content
Topics covered in this unit will include:
How will students, personally, be different as a result of the unit
Students on the unit will experience how applying AI in practice can require some detailed background knowledge of the application and an in-context understanding of the privacy, regulatory and transparency issues that apply. They will have gained an appreciation of the challenges of translating AI from forecasting and analysis to being deployed as a tool or device to be used by practitioners.
Learning outcomes
On successful completion of this unit, students will be able to:
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including pre-recorded video lectures, on-campus lecture/Q&A discussions, and formative quizzes. The unit will be supported by regular computer labs; these will provide student-centred on-campus learning through practical problem solving and will create a supportive environment where students apply for themselves the theory and methods discussed in the unit.
Tasks which help you learn and prepare you for summative tasks (formative):
Workshop discussions; online quizzes and multiple choice tests
Tasks which count towards your unit mark (summative):
Individual Coursework (70%) in which you will have a choice to focus on applications of artificial intelligence either to health or to finance, assessing Learning Outcomes 2, 4, and 5.
In-person Exam (30%), which will cover both health applications and finance applications of artificial intelligence, and will assess Learning Outcomes 1, 3, and 5.
When assessment does not go to plan:
Re-assessment takes the same form as the original summative assessment.
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. SEMT30011).
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.
