ICM520: Machine Learning, Artificial Intelligence, and Big Data in Finance

Module code: ICM520

Module provider: ICMA Centre; Henley Business School

Credits: 20

Level: 7

When you'll be taught: Semester 2

Module convenor: Dr Mininder Sethi, email: m.sethi@icmacentre.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: No

Last updated: 19 November 2024

Overview

Module aims and purpose

In this module you will learn how industry standard machine learning and data science techniques can be applied to solve predictive modelling problems in finance. You will also learn how state of the art big data techniques can be applied in finance at the level of a business problem, a complex project and at the level of a whole organisation. 

The module aims to provide students with the ability: 

• To use Python to train a computer to recognise patterns in data sets from popular finance applications using a wide range of industry standard Machine Learning models. For instance, we will look at stock price forecasting, asset classification and optimal investment selection. 
• Develop a rigorous Data Science approach to the collection, organisation and visualisation of large sets of structured and unstructured financial data. 
• Understand and be able to apply big data techniques, including artificial intelligence and large language models, to propose solutions to finance problems at the level of a business problem, a complex project and at the level of the whole organisation. 

The module places no pre-requisite of knowledge in any area of mathematics or computer science and as is designed to be easily understood by students of any academic background. The module is ideal for students in any area of finance as techniques of Machine Learning and Big Data become much more common in their application throughout all parts of the financial services industry.

Module learning outcomes

By the end of the module, it is expected that students will be able to: 

• Explain the construction of a set of industry standard machine learning models and how to interpret these models in terms of their structure and accuracy, and to use Python to apply these models over provided finance data sets and interpret the results based on a rigorous data science approach. 
• Outline and apply rigorous data science techniques for the collection and cleaning of large structured and unstructured data sets and be able to deal with issues such as missing data and outliers. 
• Explain the main issues in the distributed storage and processing of big data and how Machine Learning techniques can be applied over big data sets. 
• Discuss how Big Data techniques, including artificial intelligence and large language models, work and are changing our lives by creating new business opportunities, and how these Big Data techniques can be applied at the level of a business problem, a complex project and at the level of a whole organisation.

Module content

Topic 1: Introduction to Machine Learning and Linear Models. 

Topic 2: Classification Models (Logistic Regression and K Nearest Neighbours). 

Topic 3: Neural Networks and Deep Learning. 

Topic 4: Decision Trees and Random Forest Regressions. 

Topic 5: Numerical Techniques for Optimisation, Feature Selection and Dimensionality Reduction. 

Topic 6: Data Collection, Cleaning and Visualisation. 

Topic 7: Relational Databases, SQL and The Hadoop Model for Big Data. 

Topic 8: Cloud Computing and The Google Cloud Platform. 

Topic 9: Machine Learning, Artificial Intelligence and Business Intelligence Over Big Data. 

Topic 10: Big Data Project Management and Big Data Ethics, Big Data Case Studies.

Structure

Teaching and learning methods

• Formal lectures, in which students are strongly encouraged to ask questions. 
• Seminars, in which students are encouraged to develop their analytic skills by using Python to apply Machine Models over financial data sets. 
• Workshops to show and tell industry standard Big Data techniques. 
• Face-to-face/Online availability for student consultation.

Study hours

At least 30 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

50% weighted average mark across assessments.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Reassessment

Additional costs