IVADO Workshop

June 30, 2020 – September 3, 2020 | ONLINE (English)

Tuesdays & Thursdays, 4PM – 5:45PM

About

Language

This workshop will be given in English.

Description

The Fin-ML/IVADO Online Workshop is a practical training in machine learning, applied to concrete problems in investments, banking and insurance. Over each week, the workshop will consist of theory in one session, followed by a problem-solving session. There may be work to complete between the sessions. This online workshop will take place over a 10 week period, 1.5 hour per session with a 15 minute break.

Target Audience

Master’s and PhD students and professionals with quantitative training who want to learn about new technologies in data science, machine learning and operations research, with examples applied to problems in investments, banking and insurance.

*The concepts may be interesting to technical professionals that are not from the finance world, although examples and datasets will come from this environment.

Prerequisites

Knowledge of Mathematics & Statistics (Linear Algebra, Probability and Information Theory, Numerical Computation), and Programming (ideally Python) is strongly recommended. The participants can familiarize themselves with Chapters 2 to 4 of the Deep Learning book by Ian Goodfellow and Yoshua Bengio and Aaron Courville (available online deeplearningbook.org)

Objectives

  • Introduce students and professionals to new technologies in machine learning, that are most relevant in the financial sector;
  • Develop an understanding of the challenges and issues of data science applied to investments, banking and insurance;
  • Learn to use computer tools to solve concrete problems;
  • Encourage interdisciplinary knowledge sharing.

Organizers

  • Manuel Morales, Associate Professor, Université de Montréal, Director of Fin-ML
  • Rheia Khalaf, Director of Collaborative Research & Partnerships, Fin-ML
  • Nathalie Sanon, Head of IVADO training program

Registration

Professionals : $1300 + tx
Students : $500 + tx

Confirmed Trainers

Aden Houssein Aboubaker
MSc. Maths and Computer Science in Laval University (2018)
Student in Artificial Intelligence in Mila-Montreal University

Alexandre Carbonneau
PhD student in financial mathematics at Concordia University

Didier Chételat
Researcher
Polytechnique Montréal

Jonathan Guymont
M. Sc. Candidate in Machine Learning
MILA

Alexandre Nguyen
Machine Learning Consultant

Marie-Ève Malette
AI Scientist

Cédric Poutré
PhD candidate in financial mathematics at the University of Montreal and research assistant in high-frequency trading at IVADO

Program

Theme 1 – Weeks 1 – 2: Introduction to machine learning

Theme 2 – Weeks 3 – 4: Introduction to deep learning in investments, banking and insurance

Theme 3 – Weeks 5 – 6: Machine learning in time series forecasting

Theme 4 – Weeks 7 – 8: Introduction to Natural Language Processing in investments, banking and insurance

Theme 5 – Weeks 9 – 10: Reinforcement Learning in investments, banking and insurance

Theme 1: Introduction to machine learning

Week 1 

Theory: Cédric Poutré

  • Types of learning: supervised, unsupervised and reinforced
  • Introduction
    • Regression: Machine learning models vs. statistical approaches
  • Good practices: Overfitting and regularization
  • Good practices: Experimental design

Tutorial: Didier Chételat

  • Exploratory data analysis of an insurance dataset
  • Regression models
  • Generalisation and regularisation

Week 2 

Theory: Cédric Poutré

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

Tutorial: Didier Chételat

  • Classification models
  • Unsupervised analysis

Theme 2 – Introduction to deep learning in investments, banking and insurance

Week 3

Theory: Alexandre Nguyen

  • Introduction: Forecasting problem
  • Multilayered Perceptron
  • Convolutional neural networks (CNN)

Tutorial: Aden Houssein Aboubaker

  • Comparison between ML and DL applied in Banking (Fraud Detection) :
    • We will also see how to handle efficiently unbalanced data for this use case
  • Deep / Representation Learning applied in Insurance (Claim forecasting) :
    • How representation learning could allow to boost models
    • How to correctly apply neural network in context of mixed tabular data

Week 4

Theory: Jonathan Guymont

  • Generative adversarial network (GAN)
  • Variational Autoencoders

Tutorial: Marie-Ève Malette

  • Conditional variational autoencoders for event occurrence probability estimation

Theme 3 – Machine learning in time series forecasting

Week 5

Theory: Jonathan Guymont

  • Introduction to time series forecasting
  • Introduction to recurrent neural networks (RNN)

Tutorial: Marie-Ève Malette 

  • ARIMAX for times series prediction
  • GARCH for times series prediction

Week 6

Theory: Jonathan Guymont

  • Introduction to recurrent neural networks (continued) (RNN, LSTM, GRU)

Tutorial: Marie-Ève Malette

  • LSTM, GRU  for times series prediction
  • RNN for times series prediction

Theme 4 – Introduction to Natural Language Processing in investments, banking and insurance

Week 7

Theory: Alexandre Nguyen

  • Text processing
  • Sentiment analysis
    • Application of convolutional neural networks (CNN)
  • Embeddings and anomaly detection
    • Generative models

Tutorial: Jonathan Guymont (More information to come)

Week 8 (More information to come)

Theory: Alexandre NguyenTutorial: Jonathan Guymont

Theme 5 – Reinforcement Learning in investments, banking and insurance

Week 9

Theory: Alexandre Carbonneau

  • Introduction: learning by reinforcement
    • What is the ‘reinforcement learning problem’?
    • One popular reinforcement learning approach: Q-learning

Tutorial: Didier Chételat

  • Presentation of a financial trading environment
  • Tabular Q-learning

Week 10

Theory: Alexandre Carbonneau

  • Reinforcement learning for large-scale problems
    • Pitfalls of tabular methods of reinforcement learning
    • Function approximators for value and action-value functions 
    • Introduction to deep Q-networks
    • Real-life applications of deep reinforcement learning

Tutorial: Didier Chételat

  • Q-learning with linear function approximation
  • Experience replay

COVID-19

We are currently working remotely in order to maintain our activities.
Face-to-face training sessions and events are postponed: specific information will be communicated in each case.