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Economic and Financial Applications of Machine Learning
 

Please note to take this course you must first have completed Advanced Machine Learning & Programming in Python

The aim of this course is to present several concrete applications of ML techniques in Economics and Finance.

 

Examples of topics:

 

  • Industry: predictive maintenance and equipment monitoring

  • Retail: upselling and cross-channel marketing

  • Health and life sciences: diagnosis and risk reduction

  • Financial services: risk analysis and regulation, credit risk

  • Insurance: Fraud detection

  • Energy: demand and supply optimisation

This module can be taken alone or as part of a PG Certificate, PG Diploma or Full Masters Program. 

Key Skills

By the end of this course, participants should have knowledge and understanding of: 

Applied Machine Learning Skills: Ability to apply machine learning techniques in practical applications.

Deployment of Machine Learning Models: Skills to deploy and test machine learning models in real-life projects.

Practical Application: Hands-on experience in using machine learning for concrete applications.

Real-life Project Implementation: Applying machine learning skills to real-world projects.

Machine Learning Model Testing: Ability to rigorously test machine learning models for real-world scenarios.

Practical Deployment Expertise: Expertise in deploying machine learning models in practical settings.

Problem-Solving with ML: Addressing real-world problems through the application of machine learning.

Knowledge of Real-life ML Challenges: Understanding and navigating challenges specific to deploying ML models in real-life projects.

Desired Skills

By the end of this course, students should be able to: 

Analyse, appraise and interpret real data effectively.

Evaluate and interpret data in order to solve advanced complex problems in economics or finance.

Describe and explain their understanding of ML techniques, demonstrating enhanced knowledge of this area.

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Structure

 

Economic and Financial Applications of ML is an elective 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.

 

The total number of contact hours is 15 hours. This leaves 85 hours for private study.

There will be an assessment at the end of the course 

Lectures

 

This module consists of 2 - hour lectures per day for 5 days, plus a 1 - hour tutorial per day. 

 There will be optional clinics on the last day of the course.   

The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact edu@timberlake.co.uk.

  

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