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UMontrealX: Deep Learning Essentials

Do you want to learn how machines can learn tasks we thought only human brains could perform? Then take this Deep Learning course developed by IVADO, Mila and Université de Montréal: an extensive overview of the essentials of deep learning, this ground-breaking technology already prevalent in our lives and spanning all sectors.

5 weeks
4–6 hours per week
Progress at your own speed
Optional upgrade available

There is one session available:

7,431 already enrolled! After a course session ends, it will be archivedOpens in a new tab.
Starts Feb 22
Ends May 15

About this course

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Gain a good understanding of what Deep Learning is, what types of problems it resolves, and what are the fundamental concepts and methods it entails. The course developed by IVADO, Mila and Université de Montréal offers diversified learning tools for you to fully grasp the extent of this ground-breaking cross-cutting technology, a critical need in the field.

IVADO, a scientific and economic data science hub bridging industrial, academic and government partners with expertise in digital intelligence designed the course, and the world-renowned Mila, rallying researchers specialized in Deep Learning, created the content.

This course is based on presentations from an event held in Montreal, from September 9 to 13, 2019. It was adapted to an online course (MOOC) format and was released, for the first time, in March 2020. The tutorials' material was updated on Colab Notebook in Spring of 2021.

Mila’s founder and IVADO’s scientific director, Yoshua Bengio, also a professor at Université de Montréal, is a world-leading expert in artificial intelligence and a pioneer in deep learning as well as the scientific director of this course. He is also a joint recipient of the 2018 A.M. Turing Award, “the Nobel Prize of Computing”, for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

Deep Learning is an extension of Machine Learning where machines can learn by experience without human intervention. It is largely influenced by the human brain in the fact that algorithms, or artificial neural networks, are able to learn from massive amounts of data and acquire skills that a human brain would. Thus, Deep learning is now able to tackle a large variety of tasks that were considered out of reach a few years ago in computer vision, signal processing, natural language processing, robotics, and sequential decision-making. Because of these recent advances, various industries are now deploying deep learning models that impact various economic sectors such as transport, health, finance, energy, as well as our daily life in general.

If you are a professional, a scientist or an academic with basic knowledge in mathematics and programming, this MOOC is designed for you! Atop the rich Deep Learning content, discover issues of bias and discrimination in machine learning and benefit from this sociotechnical topic that has proven to be a great eye-opener for many.

Course created with support from


At a glance

  • Institution: UMontrealX
  • Subject: Computer Science
  • Level: Intermediate
  • Prerequisites:

    A minimal knowledge of programming (ideally in Python), basic knowledge in mathematics (linear algebra, statistics) and a scientific background are necessary to fully enjoy this course.

  • Language: English
  • Video Transcript: English
  • Associated skills:Finance, Algorithms, Natural Language Processing, Data Science, Computer Vision, Decision Making, Signal Processing, Machine Learning, Artificial Intelligence, Artificial Neural Networks, Deep Learning

What you'll learn

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At the end of the MOOC, participants should be able to:

  • Understand the basics and terminology related to 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

MODULE 1 Machine Learning (ML) and Experimental Protocol

  • Introduction to ML
  • ML Tools

MODULE 2 Introduction to Deep Learning

  • Modular Approaches
  • Backpropagation
  • Optimization

MODULE 3 Intro to Convolutional Neural Networks (CNN)

  • Introduction to CNN
  • CNN Architectures

MODULE 4 Introduction to Recurrent Neural Networks

  • Sequence to Sequence Models
  • Concepts in Natural Language Processing

MODULE 5 Bias and Discrimination in ML

  • Differences of Fairness
  • Fairness in Pre- In- and Post-Processing

Frequently Asked Questions

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What is the complete list of speakers for this course?


Golnoosh FARNADI


Jeremy PINTO

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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