Meetup Venue as of March 11th: 4th Floor of CSAIL (open collaborative space on the 4th floor across 32-G415 ). The Mission:
Develop a local Data Science community that collaborates on stimulating technical challenges.
The Kaggle Competition is a fun and technical environment for MLS members committed to advancing their skills in ML | AI | Deep Learning. We believe that working together in teams allows for crowdsourcing the best ideas and techniques.
We inspire each other to have fun, solve real-world problems and make some great friends along the way. Historically, the Machine Learning Society attracts a variety of skills and domain experts, therefore we encourage our most advanced members to mentor the next generation of explorers.
We absolutely love when new members come to us with high kaggle rankings, however we have NO tolerance for high egos or disruptive behaviour. We invite only Human 2.0 models. Thank you.
"Study Group" - (Beginner)
Members of the study group will audit several predefined courses together. List of courses required will be posted shortly.
“Kaggle Intermediate” team – (Intermediate)
This team invites students, coders of all levels and anyone that has a desire to learn more about data science through hands-on experience. No limit to team size. This team will be lead by a volunteer Machine Learning Society data scientist with extensive experience in the field. This team's challenge will be presented at the meeting.
“Competitive” team – (Advanced / Expert)
This is an advanced skills team that will have no more than 10 members. Each member will have a strong Data Science background and will pledge to contribute code for submission. This team’s challenge will be shared by the Competitive Team Leader - We provide GPUs
If the “Competitive team” wins the competition: The Machine Learning Society will keep 20% of bounty. Remaining proceeds will be divided equally between remaining team members.
Visit MLsociety.com (http://www.mlsociety.com/) to learn more about our mission. Don't forget to follow us on:
• Facebook (https://www.facebook.com/MachineLearningSociety/)
• Twitter (https://twitter.com/ML_Society)