IVADO/MILA Deep Learning School (4th and 5th edition)

September 9-13, 2019 | Montréal (English)

December 2-6, 2019 | Vancouver (English)

About

Summary

Deep learning is a machine learning technique that has significantly improved previous results in computer vision, speech recognition, machine translation and other areas. Many other areas are affected by this new technology, or will be. In response to the interest generated by this technology, and in response to training needs, IVADO and Mila are offering a full week of training in English from September 9 to 13, 2019 in Montréal. IVADO and Mila are also partnering with the UBC Data Science Institute to offer this training in Vancouver from December 2 to 6, 2019.

Target audience

The content of this school is mainly aimed at industry professionals and SMEs with basic knowledge of mathematics and programming (engineers, computer scientists, statisticians, technical project managers, product managers, systems engineers, etc.), but professors and graduate students in science or engineering (mainly those who are not yet familiar with deep learning) may also find it interesting.

Prerequisites

A minimal knowledge of programming (ideally Python) and basic knowledge in mathematics (linear algebra, statistics) is desirable.

Objectives

At the end of the training week, participants should be able to:

  • Understand the basics and terminology related to deep learning
  • Understand the methodology for carrying out a project in deep learning
  • Identify the types of neural networks to use to solve different types of problems
  • Get familiar with deep learning libraries through practical and tutorial sessions

Dates and places

Septembre 9-13, 2019, Montréal
December 2-6, 2019, Vancouver

Contact

For any comments, do not hesitate to contact us at the following email address: formations@ivado.ca.

Dates and places

September 9-13, 2019, Montréal, English

December 2-6, 2019, Vancouver, English

Program

Machine Learning

Speaker: Mathieu Germain

09:00 – 09:10 : Welcome words by IVADO and Mila

09:10 – 10:15 : Introduction to Mila & AI; Data analysis

10:15 – 10:45 : Break

10:45 – 12:00 : Machine learning tasks & experiments

12:00 – 13:30 : Lunch

13:30 – 14:45 : Machine learning tools

14:45 – 15:15 : Break

15:15 – 16:30 : Tutorial – Data & metrics with pyTorch

Deep Learning

Speaker: Gaétan Marceau Caron

09:00 – 10:15 : Introduction to deep learning

10:15 – 10:45 : Break

10:45 – 12:00 : Computational graph & backpropagation

12:00 – 13:30 : Lunch

13:30 – 14:45 : Optimization for deep learning

14:45 – 15:15 : Break

15:15 – 16:30 : Tutorial – Categorical data with multilayer perceptron (MLP)

CNN

Speaker: Jeremy Pinto

09:00 – 10:15 : Introduction to convolutional neural networks, part I

10:15 – 10:45 : Break

10:45 – 12:00 : Introduction to convolutional neural networks, part II

12:00 – 13:30 : Lunch

13:30 – 14:45 : Convolutional neural network architectures

14:45 – 15:15 : Break

15:15 – 16:30 : Tutorial – Getting started with convolutional neural networks

RNN

Speaker: Mirko Bronzi

09:00 – 10:15 : Introduction to recurrent neural networks

10:15 – 10:45 : Break

10:45 – 12:00 : Sequence to sequence models

12:00 – 13:30 : Lunch

13:30 – 14:45 : Natural language processing

14:45 – 15:15 : Break

15:15 – 16:30 : Tutorial – Recurrent neural networks

To be confirmed