Learning Outcomes:
On completing the module, students will (i) be able to demonstrate understanding of a range of optimising, modelling and forecasting techniques, (ii) be able to apply these techniques to a range of financal problems especially algorithmic trading, (iii) have acquired a high-level of expertise in using the JULIA &R programming packages, (iv) be able to critically assess the results of research studies which employ the algorithms covered on the course.
Students should understand the basics of Large Language Models (LLM's) such as ChatGPT and be aware of the general issues
their recent emergence has raised, as well as their direct applicability to Finance.
Additionally, students should demonstrate a high-level understanding of current capabilities of and directions in Machine Learning and Artificial Intelligence (including LLM's) as well as having some idea about their likely impact on Business and Society in the near future.
Indicative Module Content:
Introduction to Statistical Learning in R by Hastie and Tibshirani, et. al (2nd ed.) and R package and R Studio
Jupyter Notebooks (for JUlia PYThon R)
Machine Learning Texts TBA
Julia package release 1.9.3 or higher
Python - recent release (TBC)