Flexible learning
Online postgraduate courses
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.
- 10 Credits
- 100 hours of study
- 15 contact hours
- 85 hours for private study
Faculty
Structure
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.
Module Programme
Developing a credit scorecard for credit risk management
- Introduction to credit scoring and credit risk management
- Introduction to Basel I, II and III regulation, and PD, LGD and EAD modeling
- Data preprocessing for PD modeling
- Building PD models
- Postprocessing PD models
- Developing a credit rating system
Developing an insurance fraud detection system
- Introduction to fraud detection
- Descriptive analytics for fraud detection
- Predictive analytics for fraud detection
- Social network analytics for fraud detection
- Feature engineering for fraud detection
- Handling the imbalanced class distribution
Developing a customer churn and response model for customer relationship management
- Predictive analytics for customer churn and response prediction
- Prescriptive analytics for customer response modelling
- Introduction to causal machine learning for treatment effect modelling
- Profit-driven customer churn and response modelling
- Cost-sensitive decision-making
Foundations of factor investing
- Historical perspective (Fama-French & the factor zoo)
- Theoretical groundwork (partial equilibrium models)
- Panel models
- Practical application: portfolio sorts
- Tutorial: Fama-MacBeth regressions
Data preparation and penalized models
- Missing data and outlier management
- Penalized regressions
- Sparse portfolios (to be coded during the tutorial)
Nonlinear models
- Tree methods
- Neural networks
- Tutorial: boosted trees with alternative libraries
Parameter tuning and backtesting
- The variance-bias trade-off & the different flavours of overfitting
- Tuning methods (example: grid search)
- Principles of backtesting
- Tutorial: ML-powered backtest
Prerequisites
English Language Requirements
Both Programmes are open to applicants anywhere in the world. We may ask applicants to provide a recognised English language qualification, dependent upon their nationality and where they have studied/worked previously.
The requirement is an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We will also consider other English language qualifications. If their score is below our requirements, they may be eligible for one of Lancaster University's pre-sessional English language programmes.
Academic Requirements
Applicants to the Postgraduate Certificate of Achievement, Postgraduate Certificate, Postgraduate Diploma or full MSc in either programme require either an upper second-class degree in economics, econometrics or related subjects.
Learning Outcomes
- Applied Machine Learning Skills: Ability to apply machine learning techniques in practicalapplications
- Deployment of Machine Learning Models: Skills to deploy and test machine learning modelsin real-life projects
- Practical Application: Hands-on experience in using machine learning for concreteapplications
- Real-life Project Implementation: Applying machine learning skills to real-world projects
- Machine Learning Model Testing: Ability to rigorously test machine learning models forreal-world scenarios
- Practical Deployment Expertise: Expertise in deploying machine learning models in practicalsettings
- Problem-Solving with ML: Addressing real-world problems through the application ofmachine learning
- Knowledge of Real-life ML Challenges: Understanding and navigating challenges specific todeploying ML models in real-life projects.
- Analyse, appraise and interpret real data effectively
- Evaluate and interpret data in order to solve advanced complex problems in economics orfinance
- Describe and explain their understanding of ML techniques, demonstrating enhancedknowledge of this area.
Frequently Asked Questions
All sessions are conducted live and online at a scheduled time, but are also recorded. Students may attend live and watch the recordings back to recap the material or watch the recordings only if unable to attend live. We always advise students to attend live where possible as this will allow them the best opportunity to engage with the content and ask the lecturer's questions.
All modules are examined through online coursework submissions, you will have the support of your module lecturer/tutor in this poccess.
No, all necessary software is provided to students.
All courses are recorded and available on the LUMS internet platform throughout the current academic year. They can therefore be viewed 24 hours a day.
Timberlake Consultants and Lancaster University Management School (LUMS) Economics department have a longstanding partnership; combining 40+ years of industry expertise with over 50 years of academic excellence. We are delighted to build on this with our micro-credential postgraduate courses.
Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education
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Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education