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6 - 9 December 2021
Barcelona

6 - 9 December 2021
Barcelona

Machine Learning in Finance Workshop

Led by John Hull, Maple Financial Professor Of Derivatives & Risk Management at Joseph L. Rotman School of Management at University Of Toronto

Friday 10 December 2021

Your workshop leader

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.

Your workshop highlights

Overview

This workshop is designed for participants who are new to machine learning and want to develop their knowledge of the tools and acquire skills in this area. 

Machine Learning is now pervading all areas of finance and a good understanding of the fundamentals is becoming increasingly important for finance professionals so this workshop is designed to develop key skills such as:

  • Understanding how the algorithms work
  • Interpreting machine leaning output and making decisions on next steps in an analysis
  • Effectively communicating with data-science professionals


Morning section

Introduction

  • Types of machine learning
  • Why ML is suddenly so popular in finance
  • Methodology and terminology
  • Training, validation and test sets
  • Linear regression with many features:  ridge, lasso, elastic regression. Case study
  • Bayes classification
  • Principal components

Find out more>>


Supervised Learning

  • Logistic regression. Case study
  • Support vector machines
  • Neural networks
  • Decision trees and random forests
  • Bagging and boosting; ensemble
  • The variance-bias trade-off.

Find out more>>

Afternoon section

Unsupervised and Reinforcement Learning

  • Clustering. Case study
  • Reinforcement learning
  • Biases and data cleaning
  • Image recognition
  • Limitations of ML

Find out more>>


Other Financial Innovations

  • The pattern of innovation
  • Blockchain and hashing
  • Cryptocurrencies and ICOs
  • Roboadvisors, insurtech, and regtech
  • Kodak vs. IBM

Find out more>>