Machine Learning in Finance, Toronto

Sessions will cover neural networks, reinforcement learning, NLP and machine learning in risk management

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Machine Learning in Finance:

A Quantitative Approach

March 25–26, 2020 | Toronto

View agenda Apply to attend

The fifth edition of our machine learning course is returning to Toronto to provide attendees with an in-depth understanding of machine learning applications. 

This course will give a technical look at machine learning and develop strategies for integrating it within your organization. 

The unique multi-tutor format will provide attendees with an understanding of key theories, models, and more advanced tools in machine learning solutions through a quantitative approach that will also consider risk management, audit, fraud detection and other business areas.

What will you learn?
  • The theory behind machine learning, latest applications and how methods can be applied in your firm

  • A deeper dive in neural networks and reinforcement learning 

  • Latest approaches to machine learning applications from a quantitative viewpoint 

  • ML and AI capabilities, how they can help you solve problems more effectively and drive your business forward 

  • The importance of machine learning explainability from a risk perspective

View course agenda

Who should attend?

Relevant departments may include but are not limited to: 

  • Quantitative analysis, Quantitative modeling

  • Financial engineering

  • Data science 

  • Machine learning 

  • Portfolio management 

  • Model risk 

  • Risk management 

View pricing options

Course highlights
  • Introduction to machine learning and a tour of ML models

  • A deeper dive into neural networks, reinforcement learning and natural language processing

  • Machine learning in risk management and audit

  • The importance of explainability in finance 

  • ML in finance: putting it into practice 

  • Machine learning for fraud and Anti-Money Laundering (AML)

more details

Course speakers

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Jesús Calderón

Managing director

Maclear Data Solutions

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Rogelio Cuevas

Senior Manager, Data Scientist

TD Bank

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John Hull

Maple Financial Professor of Derivatives and Risk Management, Joseph L. Rotman School of Management

University of Toronto

John Hull, Maple Financial Professor of Derivatives and Risk Management, Joseph L. Rotman School of Management, UNIVERSITY OF TORONTO

John Hull is an internationally recognized authority on derivatives and risk management and has many publications in this area. His work has an applied focus. In 1999 he was voted Financial Engineer of the Year by the International Association of Financial Engineers. He has acted as consultant to many North American, Japanese, and European financial institutions. He has won many teaching awards, including University of Toronto's prestigious Northrop Frye award.

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Vishal Gossain

Vice President, AML / ATF Analytics

Scotiabank

Vishal Gossain is currently the vice president of global risk management responsible for all global regulatory and non-regulatory retail models, and application of artificial intelligence and machine learning for retail products. Vishal has held previous senior retail modeling, risk management, business strategy, P&L management and finance positions in Latin America and North America. Some of them include Head of credit risk for HSBC in Latin America, Head of credit risk for Capital One Canada and several other risk/business management positions in HSBC USA and consulting firms. 
Vishal currently sits on the board of MIT Computer Science and Artificial Intelligence Laboratory and University of Western Post-graduate program.
Vishal holds an undergraduate degree in engineering from Indian Institute of Technology (IIT) Kharagpur and has completed his post-graduation in engineering from University of Texas at Austin.
 

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Elliot Noma

Machine Learning Engineer Consultant

Federal Reserve Bank of New York​​​​​​​

Elliot Noma has worked in both finance and data science. He is a consultant on machine learning and natural language processing solutions at the Federal Reserve Bank of New York. He also runs a proprietary trading company, Garrett Asset Management.

Dr. Noma taught machine learning at Columbia University and currently teaches quantitative risk management in the Masters in Mathematical Finance program at Rutgers University. 

Previously, Dr. Noma was a portfolio manager running a fund of hedge funds and was the Chief Risk Officer at Asset Alliance, a $3 billion seeder of hedge funds. 

Prior to working at Asset Alliance, Elliot was a risk manager at both Merrill Lynch Investment Advisers and Deutsche Bank, was an analyst at both JP Morgan and Salomon Brother, and was a investment banker at Chase. 

He has patented a levered ETF structure, has written a book on psychometrics, and has published scholarly articles in behavioral finance, psychology, information science, sociology, math, and finance. His most recent article in 2018 describes a new method of measuring financial risk aversion using hypothetical investment preferences.

Dr. Noma has a Ph.D. in the mathematical modeling of psychological processes from the University of Michigan along with an M.A. in Mathematics. He graduated from Dartmouth College with a B.A. in Mathematics.


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Lee Medoff

CEO

Hedgehog Analytics

Lee Medoff is the founder and CEO of Hedgehog Analytics, a data and analytics consulting firm that provides analytics services and solutions, with the Financial Services sector a primary area of focus. The firm advises tech startups as well, including those in the FinTech sector.

Lee began his career in finance with the Decision Sciences group of the credit card division of JPMorgan Chase, where he developed models to optimize the return on the bank’s card portfolio. He then joined the Models and Methodologies group of the New York Fed, where as part of the Bank Supervision group he focused on Credit and Operational risk, reviewing the models banks in the 2nd District used for Basel, Economic Capital and Stress Testing purposes.   Following the Fed he moved to Moody’s Analytics Risk Management Services, where he oversaw analytics teams in New York and India developing and customizing models for the firm’s software.  He also was a consultant for the Quantitative Advisory Services Group of EY’s Financial Services Risk Management practice, where he worked on stress testing, CCAR and CECL banking and trading book projects for large US and Global financial services clients.

He holds advanced degrees in Statistics from Columbia University and Economics from New York University.

Lee enjoys reading, cycling, and all things tech and is a die-hard foodie.

CPD / CPE Accreditation

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CPD Accreditation

This course is CPD (Continued Professional Development) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event.

CPE Member

CPE Accreditation

This course is CPE (Continuing Professional Education) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event in accordance with the standards of the National Registry of CPE Sponsors.

Sheraton Centre Toronto Hotel

123 Queen St W,

Toronto, ON

M5H 2M9, Canada

Venue information

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Machine Learning in Finance, London

This training course will address in-depth the opportunities and limitations of machine learning in quantitative finance with practical guidance from a variety of expert tutors.

  • London