The course provides an introduction to the main artificial intelligence (AI) algorithms and present its applications in Finance.

Course is delivered over 2 weeks, comprising of 14 hours of lectures and 2 hours of tutorials.

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ILO being assessed

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.

The Group Presentation will not be reassessed.

The overall aim of the course is to introduce the main algorithms of AI and inform the students about is applications in Finance. The course aims to introduce Neural Networks, evolutionary programming, meta-heuristics and deep learning to students. The advantages and disadvantages of AI in Finance will be discussed along with its applications through a series of case studies and research papers. 

By the end of this course students will be able to:

1.   Understand, explain and critically assess the main Neural Networks algorithms.

2.   Understand, explain and critically assess the main evolutionary programming algorithms.

3.   Understand and critically assess the applications of Neural Networks, evolutionary programming, meta-heuristics in Finance.

4.   Understand and critically assess the concept of deep learning in Finance applications

5. Work collaboratively in a group to develop team working skills by producing a combined piece of coursework.

Students must submit at least 75% by weight of the components of the course's summative assessment.