PyData Montreal Meetup #24 (in-person | en personne)

📣 Join us for an exciting "Back to School" meetup at the Ubisoft Montreal office! 🎉🏢 Discover the power of Python in Data Journalism and Reinforcement Learning through insightful talks by our expert speakers, Roberto Rocha and Yann Bouteiller.

IMPORTANT :
* The access point is on Saint Dominique side (5480 rue saint Dominique) and NOT on Boulevard Saint laurent
* Get your proof of attendance with you (available on the meetup event page or received per email), for us to check your status (waiting list or not) at the venue

📊🤖 Don't miss this opportunity to explore cutting-edge applications and ignite your curiosity. Save the date and secure your spot now! 👉🗓️ See you there! 👋😄

Agenda

5:30 pm - Doors open

6:00 pm - Introduction

6:10 pm - How we used Python to see how many lobbyists attended Liberal fundraisers by Roberto Rocha (Investigative Journalism Foundation)

6:50 pm - Break, pizza and networking

7:10 pm - Using Video Games for Autonomous driving by Yann Bouteiller (Polytechnique Montréal)

7:50 pm - Final notes, break and networking

Talks

How we used Python to see how many lobbyists attended Liberal fundraisers

Abstract: There's nothing illegal about lobbyists supporting political parties, but it's certainly suspicious when lobbyists give money to politicians they are trying to influence. We set out to understand just how many active lobbyists attended Liberal fundraising events, which can cost up to $1,700 to attend. To do this, we crossed the guest lists of fundraisers released by the Liberal Party and Elections Canada and the federal lobby registry, which we scrape daily. This involved a complicated mix of PDF parsing, fuzzy matching, pandas wrangling, and good old-fashioned gumshoe sleuthing.

About Roberto: He is an investigative data journalist who applies data science and storytelling methods to uncover news that speaks truth to power. He currently works for the nonprofit startup Investigative Journalism Foundation, and before that worked for the CBC News investigative unit and the Montreal Gazette.

When he's not geeking out with new data-focused Python packages, he likes playing Latin jazz guitar, making pizza, shaking cocktails, and doing AcroYoga in the park.

Using Video Games for Autonomous driving

Abstract: What if a recreational video game transformed into a valuable simulator for the autonomous car industry? As companies like Tesla opt out of traditional sensors and increasingly rely on vision-based Machine Learning, this idea becomes unexpectedly relevant. Being highly optimized for visual rendering, recent editions of popular games like Forza and Need For Speed offer unparalleled trade-offs between computational complexity and visual fidelity.

In this presentation, we will explore the process of training a vision-based, real-time agent to drive in TrackMania 2020 (Ubisoft Nadeo). Despite being a visually less serious game, TrackMania's low-resource, free-to-play and creative nature makes it a well-suited proxy for this task. The final part of the talk will then open the discussion about the potential future of AI-driven bots and their capabilities.

About Yann: He is a machine learning engineer specializing in Deep Reinforcement Learning (RL) for Robotics. Currently holding the position of Research Associate at Polytechnique Montreal, Yann has made significant contributions to the field through a diverse array of publications and open-source projects, spanning from the theoretical underpinnings of Deep RL to advancements in cognitive neuroscience. He leads the development of TMRL, an open-source framework tailored to facilitate the secure and optimized implementation of ad-hoc Deep RL pipelines for complex real-time applications. This includes autonomous driving, high-frequency trading, and other high-frequency control scenarios. Notably, TMRL garners recognition for its illustrative pipeline in the TrackMania videogame.