Machine Learning 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

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Machine Learning in Finance: A Quantitative Approach

September 5-6, 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

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
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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 view of ML/AI capabilities, how they can help you solve problems more effectively and drive your business forward
  • Get to grips with modern data analysis, structured and unstructured data and new models

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Jesús Calderón

Managing Director

Gravito

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Ksenia Shnyra

Managing Partner

Exeter Consulting and Capital Management

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Alexey Panchekha

President

Turing Technology Advisors Inc

Armando Benitez

VP Trading Products, Data Science Lead

BMO

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Dr Ernest Chan

Principal

QTS CAPITAL MANAGEMENT

Dr. Ernest Chan is the Managing Member of QTS Capital Management, LLC., a commodity pool operator and trading advisor. He began his career as a machine learning researcher at IBM's T. J. Watson Research Center in the storied Human Language Technologies Group, where he built a text search engine that was ranked in the top 7 in a world-wide competition sponsored by the US Department of Defense. He later joined Morgan Stanley's Data Mining and Artificial Intelligence Group in New York. He was also a quantitative researcher and proprietary trader for Credit Suisse, Mapleridge Capital in Toronto, and Maple Financial.
Ernie is the author of "Quantitative Trading", "Algorithmic Trading", and most recently "Machine Trading", all published by Wiley. He also writes a popular blog at epchan.blogspot.com , and is an Adjunct Faculty at Northwestern University. Ernie earned his Ph.D. in physics from Cornell University, and his B.Sc. from Victoria College at the University of Toronto.

05 September 2018
2018-09-05 09:00:00 +0100

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