|Availability||Available only to MSc Quantitative Finance students|
|Contact||Lectures: 10 | Labs: 10 | Homework / Private Study: 80|
|Assessment||100% coursework. The assessment will comprise a number of individual assignments.|
|Lecturer||Dr Marc Roper|
To help students understand the more advanced approaches of evolutionary computing and machine learning for financial applications and to provide practical experience in applying them to challenging financial problems such as algorithmic trading.
On completion of this class students will:
- gain an understanding of the more advanced evolutionary computational and machine learning techniques available;
- gain an understanding of the relative advantages and disadvantages of each technique for different financial applications;
- be able to evaluate the results of a financial problem investigated using evolutionary techniques
- be able to operate applications that incorporate evolutionary approaches to computing.
The class will be presented through a mixture of lecture presentations, practical assignments and assessed projects and will include an introduction to evolutionary computing with special emphasis on financial applications. This class will focus on the more advanced approaches: neural networks and deep learning, their implementation in R (for example) and application to problems such as algorithmic trading.