About the course
About the course
This course has been developed for delegates who want a deeper understanding of the subject. The course will dig deep into areas of how to apply machine learning in quant.
This virtual training course will explore the core components of machine learning from objective function to model interpretation and validation. It will further your skills to apply them to enhancing machine learning models, pricing, and how to detect anomalies.
What will you learn?
- Gain a comprehensive introduction to machine learning models and their applications in finance
- Understand practical techniques to apply machine learning techniques on low-frequency events as they appear in risk management.
- Gain insights on the relationship between model complexity and model explainability, as well as its consequences in financial applications.
- Understand what is meant by deep learning and how these models compare against other techniques.
- Gain an understanding of current challenges, regulatory impacts and considerations for model risk management.
Who should attend?
Course participants include professionals interested in incorporating Machine Learning in their practice:
- Quants and derivatives pricing specialists
- Risk managers
- Model risk managers and model validation experts
- Financial regulators
- AML specialists, comptrollers and auditors
- The opportunity to listen to experts in the field who will give a clear foundation understanding of machine learning and use cases in financial services.
- You will gain insights to practically implement machine learning models in your organization.
- Gain a broad perspective of available machine learning techniques, their relative strengths and weaknesses, as well as a perspective on how to select an appropriate approach for different types of problems.