About the course
About the course
What will you learn?
- Understand the difference between modern statistics, ML and AI
- Why machine learning is becoming more important to financial organisations
- The importance of identifying data sources and types
- Understand the principles of model and variable selection
- The differences between supervised and unsupervised learning
- Approaches to satisfy explainability
- How to identify and monitor risk in ML
Who should attend?
Relevant departments may include but are not limited to:
- Quantitative Development and Trading
- Risk Management
- Model Validation
- Data Science
- Machine learning
- Portfolio Management
- Introducing machine learning (ML)
- Understanding data
- Objective function
- From modern statistics to machine learning models
- Interpreting and validating a model
- Machine learning in investment management and portfolio optimisation
- ML for risk practitioners
- Machine learning in finance: putting it into practice
This introductory course has been developed for delegates new to the subject, or wanting to refresh their knowledge of the fundamental principles of machine learning and how it applies to financial organisations.
This training course will explore the core components of machine learning from objective function to model interpretation and validation.Attendees will have the opportunity to learn about the importance of data quality and how to identify, process and analyse data appropriately.
Sessions will address the parameters of model selection and explainability and how machine learning can then be applied to financial risk, investment management and portfolio strategies.