COMP0050 Machine Learning with Applications in Finance

This database contains the 2018-19 versions of syllabuses. These are still being finalised and changes may occur before the start of the session.

Syllabuses from the 2017-18 session are available here.

Academic session

2018-19

Module

Machine Learning with Applications in Finance

Code

COMP0050

Module delivery

1819/A7P/T2/COMP0050 Postgraduate

Related deliveries

None

Prior deliveries

COMPG014

Level

Postgraduate

FHEQ Level

L7

FHEQ credits

15

Term/s

Term 2

Module leader

Caccioli, Fabio

Contributors

Caccioli, Fabio

Module administrator

Nolan, Martin

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 will perform their own data analyses in MATLAB.

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.Supervised learning methods and examples of their application in Finance;
  2. understand unsupervised learning methods and examples of their application in Finance.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MSc Computational Finance
  • MSc Financial Risk Management

Prerequisites:

In order to be eligible to select this module, students must:

  • be able to write a reasonably non-trivial computer program in MATLAB;
  • have an understanding of basic levels of probability theory, linear algebra, and multivariate calculus;

Students who select this module may be required to attend the course “Introduction to MATLAB for Finance”.

Content

Basics on Probability, Multivariate Calculus, and Linear Algebra.

To be studied independently by students.

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.

An indicative reading list is available via http://readinglists.ucl.ac.uk/departments/comps_eng.html.

Delivery

30 hours of lectures plus homework and assignments.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

Written examination (2hrs 30mins)

50

 

2

Report

30

 

3

Report

20

 

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.