This site is part of the Informa Connect Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them. Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 3099067.

Machine learning in finance workshop

Led by John Hull, Maple Financial Professor, University of Toronto

Friday 6 December 2019. Separately bookable.

Your workshop leader

John Hull

Maple Financial Professor Of Derivatives & Risk Management

Joseph L. Rotman School of Management at 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.

Workshop highlights


This workshop is designed for participants who are new to machine learning and want to acquire skills in this area.  In order to succeed in finance, individuals need to understand enough about machine learning to use it as a tool. This means that they must (a) understand in a general way how the algorithms work, (b) know how to interpret machine leaning output and be able to make decisions on next steps in an analysis, and (c) communicate effectively with data-science professionals. The workshop will focus on developing these skills.

Modules 1 and 2
Modules 1 and 2

How data science is changing finance

  • Improvements in computer processing speeds and data storage
  • Disintermediation and re-intermediation
  • The terminology of machine learning
  • The variance-bias trade-off

Underlying tools

  • Gradient descent algorithms
  • The sigmoid function
  • Linear and logistic regression
  • Case study
Modules 3 and 4
Modules 3 and 4

Clustering and neural Networks

  • K-means and other clustering algorithms
  • Neural networks
  • Classification vs. value prediction
  • Case study

Other algorithms

  • Support vector machines
  • Decision trees
  • Bagging, boosting and random forest
  • Case study