Machine Learning Toronto

Sessions will cover machine learning models, recent trends in machine learning and its application to quantitative finance, risk and portfolio construction.

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Machine Learning in Finance: A Quantitative Approach

28-29 March, Toronto

View the Agenda    Early Bird Pricing

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.

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Who Should Attend

Relevant deparments may include:

  • Machine Learning
  • Portfolio Management
  • Asset Allocation
  • Data Science
  • Financial Engineering
  • Quantitative Analytics
  • Quantitative Modelling
  • Innovation
  • Forecasting
  • Infrastructure and Technology
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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
     

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