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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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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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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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 modern data analysis - structured and unstructured data and new models
  • Understand challenges related to data infrastructure and technology

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Ajinkya Kulkarni

Director, Artificial Intelligence and Machine Learning

SCOTIABANK

Ajinkya (pronounced A-jink-ya) is a seasoned leader and specializes in commercializing promising technology innovations.

Over his career, he has helped large enterprises and startups build technologies ranging from mobile apps to big data and in latest continuation, Artificial Intelligence in the financial sector.

Starting with early iterations of RPA (Robotic Process Automation) and Log analytics, Ajinkya has delivered business results by applying machine learning in all forms, namely statistical modeling, machine learning, network analytics and most recently, neural networks and deep learning. He has applied his skills across many verticals and major brands, IBM, Accenture, TELUS and Scotiabank.

An Engineer by profession, Ajinkya is a graduate of the Master of Management of Innovation program at the University of Toronto. He is an avid photographer and enjoys learning languages in spare time.

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Tarundeep Dhot

Associate Partner, Cognitive and Advanced Analytics

IBM Global Business Services

Tarun is a member of the 2017 North American Financial Information Summit Advisory Board - view the list of all 12 esteemed board members by clicking here

Tarun Dhot is the Director of Advanced Analytics at the Canadian Imperial Bank of Commerce (CIBC). Tarun leads a group of Data Scientists that specialize in deriving insight from CIBC's large unstructured and structured information sources using advanced analytical techniques. Previously, Tarun held leadership roles in Decision Science, Fraud Strategy and Operations within CIBC. He holds a Master's degree in Computer Science specializing in Artificial Intelligence and a Bachelors in Electrical Engineering. Tarun is a Lean and Six Sigma Certified Professional. Tarun has previously collaborated with multiple research organizations including the Los Alamos National Laboratory and Google.

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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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Ernest Chan

Principal

QTS Capital Management

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

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Machine Learning London

With the recent success of our Canadian training course, Risk Training is happy to announce that our Machine Learning in Finance: A Quantitative Approach is coming to London this September!

  • London