Constructor University x BMW Group

Causal Machine Learning Hackathon

November 29 - December 1, 2024
Register by November 27, 12 AM CET to secure your spot!

When Friday, November 29, 2024
at 08:00
Where
Campus Centre
Price
Free
Causal Machine Learning Hackathon banner image

This three-day Causal Machine Learning Hackathon offers a unique opportunity to tackle real-world challenges using advanced analytics and causal machine learning techniques for a leading global player in the automotive sector: BMW. Throughout the three-day event, you’ll have the opportunity to work alongside BMW experts and Constructor University faculty (supported by "INDEED - Data Driven Collaborative Decision Making in Complex Industrial Systems"), who will be there every step of the way to provide guidance and feedback. Do not miss this incredible chance to gain hands-on experience, expand your skills, connect with industry leaders, and take home the top prize!

Recommended Skills and Background

Who should apply? We are primarily looking for students with experience in Computer Science and programming—but students from all backgrounds are welcome and encouraged to participate. We recommend being well-versed in the following: 

  • Machine Learning
  • Proficiency in Python and/or R
  • Angular and/or Streamlit for data visualization
  • Data Science and Advanced Analytics 

Additional knowledge in Causal Machine Learning or Causal Inference is a bonus! 

 

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students learning on computers
Team Setup

Participants can choose to work individually or in teams of up to 4 members. You are free to form your team as you wish; there will be no pre-screening process.

Why Take Part?
  • Winners will receive a direct interview opportunity for internship positions at BMW—bypassing the technical round —as well as a personalized letter of recommendation on BMW letterhead.
  • The winning team will be featured on BMW Group's official social media platforms, including LinkedIn and Facebook, with a combined reach of over 3 million followers. This is a fantastic opportunity for increased visibility and professional recognition.  
  • The top 3 teams will also receive exclusive branded merch!
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Students in front of the IRC building at Constructor University
We're Fully Booked

No spots left for now. Keep tuned in case additional tickets are released!

Hackathon Schedule
TimeDay 1
Friday, November 29
Day 2
Saturday, November 30
Day 3
Sunday, December 1
Day 4
Monday, December 2
From 8:30 AMRegistration       WorkdayWorkday and SubmissionGrading and Review
9:00 AM - 10:00 AMWelcome and Introduction
10:00 AM - 11:00 AMProblem Explanation and Data Overview
11:00 AM - 2:00 PMHackathon Kickoff - Data & Task Distribution on GitHub
2:00 PM - 5:00 PM
5:00 PM - 6:30 PM   Awards Ceremony, Photoshoot & Interview with the Winning Team
What You Need

To take part, you’ll need the following:

  • Hardware: You’ll need to bring your laptop—Don’t forget your charger!
  • Brain fuel: Drinks will be provided. But please bring your own meals and/or snacks to keep energized throughout the event. 
Provided Resources
Data and task details
All necessary data and tasks will be shared with participants via GitHub at the start of the event.
Presentation materials
All required presentation materials (PPT, PDF templates) will be provided at the beginning of the hackathon.
Expert guidance
Support and guidance for machine learning and causal analytics will be available throughout the event to help you succeed.
Guidance and Mentorship
  • Each team will have a mandatory session of 10-15 minutes with BMW experts on Day 1.
  • Additional support will be available at the Causal Machine Learning booth, staffed by the BMW team and Indeed Workgroup.
  • Students are free to work beyond regular hours, with experts available on campus during working hours for guidance.
Deliverables

Teams are expected to submit the following by the end of the hackathon:

  • Tool: A clear visualization of the problem solution, with a focus on explainability.
  • Model quality: Explanation and results of the applied machine learning model.
  • Prescriptive analytics: Recommendations based on the model’s outputs.
  • Out-of-the-box thinking: Unique, innovative approaches applied to the problem. 
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students outdoor
Final Presentations

Each team must submit their findings, presentation video, as well as the above-mentioned deliverables by 11:00 PM on Day 3. Presentation videos should not exceed 5 minutes per team.

Got questions? Contact us at events@constructor.university