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 

  • Location intelligence and its applications 

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 

  • Location Intelligence 

  • Machine Learning and Model Risk 

  • Machine Learning in Finance: Putting it into Practice 

View Course Agenda

Course speakers

Jesús Calderón

Managing Director


Rogelio Cuevas

Senior Manager, Data Scientist

TD Securities

Arthur Berrill

Head of Content and Location Services, DNA


Arthur Berrill is the Head of Content and Location Services at the Royal Bank of Canada. He has a charter to research, design and guide the use of content and specifically location intelligence across all departments of the bank.

Arthur is a founding board member of the Location Forum that develops best practices and guidelines in the use of location data and technology. Arthur is also the chair of the CRCSI Research Investment Committee, a board responsible for guiding research directions and funding in spatial analytics and applications of spatial technology. And Arthur is a member of the TECTERRA Advisory Committee providing evaluations and guidance on the direction of funded geomatics research and innovation projects.

Arthur has almost 40 years of experience in the architecture, design and development of enterprise spatial systems including WILDMAP (a GIS before the term existed), SYSTEM 9, SpatialWare®, MapInfo products and Location Hub®. He holds numerous key patents in the location intelligence and spatial systems domain.

Fascinated by the use of new technology to solve business challenges, Arthur is working on new or improved algorithms and methods borrowed from other disciplines (such as genetics, deep learning), reinvented for modern architectures (such as massively parallel, share nothing) or imposed on the spatial disciplines by the evolution of technology (such as big data and spatial federation).

Prior to RBC, Arthur led the location intelligence initiative at Scotiabank and before then was president of DMTI Spatial. Arthur was Inventor of the Year for 2008 at Pitney Bowes (MapInfo) and won the TechAmerica 50th Anniversary Innovation Award in 2009.

Arthur is a graduate with Honours from the University of Queensland and did his postgraduate work at the International Institute of Aerial Survey and Earth Sciences in the Netherlands.

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.

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.

Model Risk Australia

This two-day workshop has been designed to delve into best practice approaches to building a model risk framework. Attendees will be equipped with a thorough understanding of model risk now and into the future, including the impact of machine learning.

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XVA Australia

A two-day workshop will provide attendees with an overview of the current challenges facing the industry in the world of XVA.

  • Sydney, Australia

Cyber Risk

Cyber Risk Thailand

  • Thailand

Countdown to the Course

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