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 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 place
December 2-6, 2019, Vancouver
UBC Nest South Ballroom
6133 University Boulevard
Vancouver, BC
Registration
Registration will open in June. The opening of registrations will be announced in the IVADO newsletter and on our social networks.
Ressources (French)
Contact
For any comments, do not hesitate to contact us at the following email address: formations@ivado.ca.
This school is made possible by the Canada First Research Excellence Fund.
Dates and places
Confirmed Speakers
Gaétan Marceau Caron
Applied Research Scientist
Mila – Institut Québécois d’Intelligence Artificielle
Michiel van de Panne
Professor, Canada Research Chair
Associate Head for Research and Faculty Affairs
Fred Popowich
Computing Scientist at Simon Fraser University
Scientific Director of SFU’s Big Data Initiative
Raymond T Ng
Director, Data Science Institute, UBC
Professor, Computer Science, UBC
Canada Research Chair in Data Science and Analytics
Chief Informatics Officer, PROOF (Prevention of Organ Failure) Centre
Program
Machine Learning
9AM – 09:10AM: Welcome
Welcome words by IVADO and Mila
09:10AM – 10:15AM: Presentation
Machine learning and experimental protocol
Gaétan Marceau Caron
10:15AM – 10:45AM: Break
10:45AM – 12PM: Presentation
Introduction to Machine Learning
Gaétan Marceau Caron
12PM – 1:30PM: Lunch
1:30PM – 2:45PM: Presentation
Machine learning tools
Jeremy Pinto
2:45PM – 3:15PM: Break
3:15 – 4:30PM: Tutorial
Data & metrics with pyTorch
Deep Learning
9AM – 10:15AM: Presentation
Introduction to deep learning
Gaétan Marceau Caron
10:15AM – 10:45AM: Break
10:45AM – 12PM: Presentation
Computational graph & backpropagation
Gaétan Marceau Caron
12PM – 1:30PM: Lunch
1:30PM – 2:45PM: Presentation
Optimization in deep learning
Gaétan Marceau Caron
2:45PM – 3:15PM: Break
3:15PM – 4:30PM: Tutorial
Categorical data with multilayer perceptron (MLP)
CNN
9AM – 10:15AM: Presentation
Introduction to convolutional neural networks, part I
Jeremy Pinto
10:15AM – 10:45AM: Break
10:45AM – 12PM: Presentation
Introduction to convolutional neural networks, part II
Jeremy Pinto
12PM – 1:30PM: Lunch
1:30PM – 2:45PM: Presentation
Convolutional neural network architectures
Jeremy Pinto
2:45PM – 3:15PM: Break
3:15PM – 4:30PM: Tutorial
Getting started with convolutional neural networks
RNN
9AM – 10:15AM: Presentation
Introduction to recurrent neural networks
Mirko Bronzi
10:15AM – 10:45AM: Break
10:45AM – 12PM: Presentation
Sequence to sequence models
Mirko Bronzi
12PM – 1:30PM: Lunch
1:30PM – 2:45PM: Presentation
Natural language processing
Mirko Bronzi
2:45PM – 3:15PM: Break
3:15PM – 4:30PM: Tutorial
Recurrent neural networks
9AM – 10:15AM: Presentation
Reinforcement Learning
Dr. Michiel van de Panne (Professor, UBC Computer Science)
10:15AM – 10:30AM: Coffee Break
10:30AM – 12PM: Presentation
Reinforcement Learning
Dr. Michiel van de Panne (Professor, UBC Computer Science)
12PM – 1PM: Lunch Break
1PM – 2:45PM: Presentation
Ethics in AI: Bias and discrimination in Machine Learning
Dr. Golnoosh Farnadi (Researcher at Polytechnique Montréal)
2:45PM – 3:00PM: Coffee Break
3:00PM – 4:00PM: Presentation
Ethics in AI Discussion Panel
-
- Evgueni Loukipoudis
CTO of Canada’s Digital Technology Supercluster. - Fred Popowich
Computing Scientist at Simon Fraser University
Scientific Director of SFU’s Big Data Initiative. - Raymond T Ng
Director, Data Science Institute, UBC
Professor, Computer Science, UBC
Canada Research Chair in Data Science and Analytics
Chief Informatics Officer, PROOF (Prevention of Organ Failure) Centre - Golnoosh Farnadi
IVADO post-doctoral fellow at Polytechnique Montréal
- Evgueni Loukipoudis