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.

RiskMinds International
18 - 21 November 2024
InterContinental O2London

Machine Learning in Finance Workshop

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

Monday 7 November

Your workshop leader

John Hull

Maple Financial Professor Of Derivatives & Risk Management

Joseph L. Rotman School of Management at University Of Toronto

John is an internationally recognized authority on derivatives and risk management with many publications in this area. 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 and 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.

Book the workshop with your main conference ticket to save £100!

Modules 1 and 2

Introduction and Unsupervised Learning

  • Machine learning vs statistics
  • Different learning approaches and their applications
  • The use of training, validation, and test data sets
  • The variance-bias trade-off
  • The k-means algorithm and case study
  • Other unsupervised learning tools

Supervised learning

  • Linear and logistic regression
  • Ridge, lasso, elastic net. Case studies
  • Support vector machines
  • Decision trees and random forests
  • Bagging and boosting; ensemble models
  • Neural networks and the gradient descent algorithm

Modules 3 and 4

Reinforcement Learning

  • Exploration vs exploitation
  • Monte Carlo method
  • Examples
  • Temporal difference learning
  • Deep Q-learning
  • Applications

NLP and Model Explainability

  • Sentiment analysis
  • Bag of words models
  • Other applications
  • Importance of explainability
  • Shapley values
  • LIME