Workshop – Finance and Insurance

MAY 13-17, 2019 | MONTRÉAL (English)

JUNE 10-14, 2019 | MONTRÉAL (French, IVADO members only)

About

Summary

The Fin-ML/IVADO Workshop is a one-week practical training in machine learning, applied to concrete problems in finance and insurance. This workshop will consist of theory in the morning, followed by problem-solving workshops in finance and insurance in the afternoon.

The workshop will be held in English. Participants will have to bring their laptops for the practical part, no special installation is required.

Objectives

  • Train professionals in new technologies in data science, machine learning and operations research;
  • Develop an understanding of the challenges and issues of data science applied to a specific field;
  • Learn to use computer tools to solve concrete problems;
  • Foster knowledge sharing and facilitate networking among specialists in a particular field;
  • Encourage interdisciplinary knowledge sharing.

Prerequisites

Basic knowledge of mathematics and programming (ideally Python) is strongly recommended.

Organizers

  • Manuel Morales, Université de Montréal
  • Rheia Khalaf, Université de Montréal / IVADO
  • Brian Moore, IVADO

Contact

For any inquiries, please contact us at formations@ivado.ca.

Dates and place

May 13-17, 2019: The workshop will be held in English at HEC Montréal (PWC, Côte Ste-Catherine).

June 10-14, 2019: The workshop will be held in French at HEC Montréal (PWC, Côte Ste-Catherine).

Program

Introduction to machine learning

SCHEDULE:

09:00 – 09:30: Registration and light breakfast

09:30 – 12:30: Theory 

  • Introduction
    • Logistic regression and regression: Machine learning vs. statistical approaches
  • Types of learning: supervised, unsupervised and reinforced
  • Good practices: Overlearning
  • Good practices: Cross-validation and experimental design

12:30 – 13:30: Lunch

13:30 – 16:30: Tutorial

  • Framework presentation : Python, Keras, Pytorch.
  • Some illustrative examples.

Two coffee breaks at 11:00 and 15:00

Supervised and unsupervised learning

SCHEDULE:

09:00 – 09:30: Registration and light breakfast

09:30 – 12:30: Theory 

  • Introduction : Classification problem
    • Traditional approaches : SVM, Random Forests, etc
    • Modern approaches : neural networks
    • Good practices
  • Introduction : Clustering problem
  • Traditional approaches : K-means
  • Modern approaches : Embeddings

12:30 – 13:30: Lunch

13:30 – 16:30: Tutorial

  • Keras Tutorial: Data mining in insurance

Two coffee breaks at 11:00 and 15:00

Neural networks

SCHEDULE:

09:00 – 09:30: Registration and light breakfast

09:30 – 12:30: Theory

  • Introduction: Forecasting problem
  • Multilayered Perceptron
  • Introduction to recurrent neural networks (RNN)
  • Good practices

12:30 – 13:30: Lunch

13:30 – 16:30: Tutorial

  • Pytorch tutorial: Data mining in finance

Two coffee breaks at 11:00 and 15:00

Introduction to NLP

SCHEDULE:

09:00 – 09:30: Registration and light breakfast

09:30 – 12:30: Theory

  • Text processing
  • Sentiment analysis
    • Application of convolutional neural networks (CNN)

Embeddings and anomaly detection

  • Generative models

12:30 – 13:30: Lunch

13:30 – 16:30: Tutorial

  • Case study: Spam detection and sentiment analysis

Two coffee breaks at 11:00 and 15:00

Reinforcement learning

SCHEDULE:

09:00 – 09:30: Registration and light breakfast

09:30 – 12:30: Theory

  • Introduction: learning by reinforcement
    • Q-learning

12:30 – 13:30: Lunch

13:30 – 16:30: Tutorial

  • Case study: Reinforcement learning in finance

Two coffee breaks at 11:00 and 15:00