Machine Learning Toronto

Sessions include recent trends in machine learning application to quant finance, model risk, portfolio construction and location intelligence


Machine Learning in Finance:

A Quantitative Approach

Toronto, 25 - 26 September 2019

Course guide Apply now

The fourth edition of our machine learning training course will provide delegates with an in-depth understanding of machine learning applications. This course is designed to give 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 theories, models and more advanced tools in machine learning solutions through a quantitative approach that will also consider risk management and other business areas.

What Will You Learn?
  • In-depth understanding of how machine learning is changing the financial industry landscape 

  • The theory behind machine learning, its latest applications and how these methods can be applied in your organization 

  • 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 

  • Machine learning models and a deep dive into neural nets and reinforcement learning  

View course guide

Who Should Attend?

Relevant departments may include but are not limited to: 

  • Quantitative Analysis 

  • Data Science 

  • Machine Learning 

  • Portfolio Management 

  • Financial Engineering 

  • Model Risk 

  • Risk Management 


View pricing options

Course Highlights
  • Introduction to Machine Learning and Early Financial Applications 

  • Machine Learning Models

  • Machine Learning and Risk 

  • Explain Yourself – The Importance and Explainability in Finance

  • Deeper Dive into Neural Nets and Reinforcement Learning 

  • Machine Learning and Model Risk 

  • ML in Finance: Putting it into Practice 

View Course Agenda

Course speakers

Jesús Calderón

Managing Director


Rogelio Cuevas

Senior Manager, Data Scientist

TD Securities

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.

Jonathan Mikkila

Senior Machine Learning Developer


Vishal Gossain

Vice President, AML / ATF Analytics


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.

Terry Benzschawel

Founder and Principal

Benzschawel Scientific

Terry Benzschawel recently started his own firm after 30 years as a quant on Wall Street. The firm specializes in financial education, advanced model development, and systematic trading. Before that, Terry was a Managing Director in Citigroup's Institutional Clients Business, heading the Quantitative Credit Trading group.
Terry received a Ph.D. in Experimental Psychology from Indiana University (1980) and his B.A. (with Distinction) from the University of Wisconsin (1975). Terry has done post-doctoral fellowships in Optometry, Ophthalmology, and engineering prior to embarking on a career in finance. Terry began his financial career in 1988 at Chase Manhattan Bank, building genetic algorithms to predict corporate bankruptcy. In 1990, he moved to Citibank and trained a neural network to detect fraud on credit card transactions. In 1992 he was hired by Salomon's Fixed Income Arbitrage Group to build models for proprietary fixed income trading. In 1998, he moved to Citi’s Fixed Income Strategy department as a credit strategist with a focus on client-oriented solutions across all credit markets where he worked in related roles since then. Terry is a frequent speaker at industry conferences and events and has lectured on credit modelling at major universities and government institutions. In addition, he has published over a dozen articles in refereed journals and has authored two books:  CREDIT MODELING: FACTS, THEORIES AND APPLICATIONS and CREDIT MODELING: ADVANCED TOPICS.

Steve Yalovitser

Co-Founder, New York Quantum Computing Meet-up and Director, XVA Quant Core Lead

Wells Fargo

Steve Yalovitser is a Director of Quantitative Strategies Group, Global Banking and Markets, within Bank of America Merrill Lynch's Counterparty Portfolio Management (CPM) Group. Yalovitser has been a lead architect for the bank, with exposure to a wide variety of asset classes, for the past 13 years. He has delivered multiple innovation-driven technology solutions for the bank, including its first equity exotics booking platform, its first equity back-testing platform and its first ad hoc scenario platform for Capital Calculations.

Yalovitser founded, led and delivered the Quartz Equity Derivatives Risk eco-system, currently running a portion of Bank of America Merrill Lynch's end-of-day risk reporting function. He is currently working on building out a Strategy Platform for CPM, covering Counterparty Valuation Adjustment (CVA), Capital Valuation Adjustment (KVA), Funding Valuation Adjustment (FVA), and Initial Margin (IM) posting.

Before joining Bank of America Merrill Lynch, Yalovitser founded Integrasoft LLC, creating the first product to address data aspects of grid computing and implementing it for use in derivative pricing applications for potential client sites. Prior to that, he held lead architect roles at several firms, including DoubleClick, Morgan Stanley and Dow Jones.


Sheraton Centre Toronto Hotel

123 Queen St W,

Toronto, ON

M5H 2M9, Canada

Venue information


CPD / CPE Accreditation


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.

Non-financial Risk Management NY

This two day training course will provide attendees with best practice approaches to governance and creating effective NFR and ERM frameworks and cover new and emerging areas of non-financial risk.

  • New York

Essentials of Operational Risk Hong Kong

This course will bring together operational risk professionals from across the region and provide an unmatched opportunity to learn about and discuss a wide diversity of operational hazards and the evolving thinking on how to handle them.

  • Sheraton Hong Kong Hotel & Tower

Countdown to the Course

25 September 2019
2019-09-25 09:00:00 +0100