¹û¶³Ó°Ôº

Aims:

This module aims at introducing students to basic ML tools, covering both supervised and unsupervised learning methods. We discuss some of the underlying principles and students will develop practical skills to use these methods in financial applications. In their coursework, students can perform their own data analyses in the language they prefer (the suggested one being MATLAB).

Intended learning outcomes:

On successful completion of the module, a student will be able to:

1. Understand the general background of ML methods and their differences to standard methods in Financial Econometrics and Statistics.
2. Understand unsupervised learning methods and examples of their application in Finance.

Indicative content:

The following are indicative of the topics the module will typically cover:

General Introduction to Machine Learning:

• History and background; classification of ML approaches; overview of applications.

Introduction and Applications of Supervised Learning:

• Linear regression; model selection and regularization; feature selection; logistic regression; regression trees and forests; support vector machines; neural networks.

Introduction and Applications of Unsupervised Learning:

• Distance/similarity measures; clustering approaches; principal component analysis.

Introduction to Reinforcement Learning.

Requisites:

To be eligible to select this module as optional or elective, a student must: (1) be registered on a programme and year of study for which it is a formally available; (2) have an understanding of basic levels of probability theory, linear algebra, and multivariate calculus; and (3) be able to write a reasonably non-trivial computer program in MATLAB.