Machine Learning Toronto
Sessions will cover machine learning models, recent trends in machine learning and its application to quantitative finance, risk and portfolio construction.
This two day training course will provide delegates with an in-depth understanding of machine learning applications. This course will be a technical look at machine learning and provide suggestions and strategies for integrating it within your organization.
The multi-tutor format will provide attendees with an understanding of key theory, models, and more advanced tools in machine learning solutions through a quantitative approach that will also consider portfolio construction, trading, risk management and other business areas.
Who Should Attend
Relevant deparments may include:
- Machine Learning
- Portfolio Management
- Asset Allocation
- Data Science
- Financial Engineering
- Quantitative Analytics
- Quantitative Modelling
- Infrastructure and Technology
What Will You Learn?
- Best practice approaches to machine learning applications in finance from a quantitative viewpoint
- The capabilities of machine learning tools in portfolio construction, trading, risk management and beyond
- Insight into the big data revolution and the building blocks of machine learning tools in finance
- The theory behind machine learning, deep learning and neural networks, and how these methods can be applied in your organization
- A clear new of ML/AI capabilities, how they can help you solve problems more effectively and drive your business forward
- Future opportunities, regulatory expectations and the evolving landscape of ML
This course will provide a comprehensive understanding of the mathematical foundations of AAD and its role within financial applications
Following success in London, we are bringing our Trade Surveillance for Energy Trading Firms course to Amsterdam for the first time. Covering topics such as effective monitoring and complying with market abuse regulation, governance and the effects on the compliance function.