Machine Learning in Finance: A Quantitative Approach
March 28-29, Toronto

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:

This course is primarily aimed at those working in financial institutions; as well as regulatory bodies, advisory firms and technology vendors. However Risk Training welcomes anyone who would benefit from this training. Specific job titles may include:

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

Course Highlights:

  • Unique multi speaker format featuring sessions from key industry practitioners and academics
  • Get insight into the big data revolution and the building blocks of machine learning tools in finance
  • Understanding machine learning methodology from a quantitative viewpoint
  • Learn the theory behind machine learning, deep learning and neural networks, and how these methods can be applied in your organization
  • Gain insight into the latest and most widely used industry applications
  • Get a clear view of ML/AI Capabilities in finance, how they can help you solve problems more effectively and drive your business forward

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Learning Outcomes

  • Understand where the industry currently stands with regards to machine learning applications in finance from a quantitative viewpoint
  • Discuss classical and advanced models
  • Learn about capabilities of machine learning tools in portfolio construction, trading, risk management and beyond
  • Get to grips with the modern data analysis - structured and unstructured data and new models
  • Understand challenges related to data infrastructure and technology
  • Discuss opportunities and the future of machine learning in capital markets

 

Risk Training Q1 2018 Calendar
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