CAIS X 2022 ~ January – April

Pre-Registration

Pre-registering for the event does not guarantee a spot but does provide you access to our email list and important information regarding the event. Please ensure that you use an email you check regularly to see the monthly communications before the competition begins.

Welcome

Welcome to Carleton Artificial Intelligence Society’s official undergraduate machine learning competition! In this competition you will compete for cash prizes while hopefully learning a lot about machine learning and artificial intelligence.


This competition will be run from January 1st 2022 12:00 am EST to April 1st 2022 at 11:59 pm EST.
Please read the rules below. If this is your first time, please read up on how to submit your first Kaggle notebook as well.


Other than that, if you have any questions, feel free to post it in the Kaggle discussion forum or on our Discord.

The Data

What influences students to do well in school? Is it the income their parents make or the number of extracurriculars students do? Is it both? Neither?

This is what you’re going find out in the next CAIS X Competition. To find out more, pre-register and come back to the Kaggle page when the competition opens!

 

Competition Timeline

Start Date: January 1st, 2022

End Date (Final Submission Deadline): April 1st, 2022, 11:59 pm EST

Top 5 Teams Announced: April 5th,  2022, 5:00 pm

Submission Limits

You may submit a maximum of 10 entries per day. You may only select 1 final submissions for judging.

Prizes and Judging

Cash prizes of $100, $50 and $25 will be awarded to first, second and third place.

However, the top 5 scores will be chosen for further judging on the overall quality of their code and solution by our team, as well as being scrutinized for plagiarism or other forms of cheating.*

This means that if you’re first place in terms of score, it does not mean you will win first prize. Your code, alongside having a high score, needs to be efficient and high quality, having proper coding practices like comments and organization in order to win.

As well, in order to receive any prizes, your code needs to be open source in your choice of platform (Kaggle, GitHub, etc…) after the winners have been announced.

*If 3 winners cannot be chosen from the top 5 because of rule breaks, 6th place will be evaluated, and so on until 3 winners can reasonably be announced.

All teams, regardless of place, are also strongly encouraged and invited to publish a manuscript of their solution (and open source their code, if willing).

How to Enter

  1. Visit this URL kaggle.com/c/caisx/overview
  2. Sign in or create a Kaggle account
  3. Click on the Rules tab and read the rules
  4. Click on the “Join Competition” button
  5. Start hacking away!

 

Evaluation

The evaluation metric for this competition is Root Mean Square Error (RMSE). The Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit.

 

Submission Format

For every student in the dataset, submission files should contain two columns: studentID and G3. See the example below

The file should contain a header and have the following format:

StudentID,G3
901,15
902,8

Competition Workshops

Interested in learning more about the CAIS X? Need some help getting started? Click the images below to learn more about these workshops!

Past workshops have a Youtube recording you can refer to at any time.

Interested in all of our workshops? Check out our website carletonai.com/events

 

Official Rules

1. Kaggle

 

You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts. All Kaggle.com rules are expected to be followed: https://www.kaggle.com/terms.

 

2. No private sharing outside teams

Privately sharing code or data outside of teams is not permitted. It’s okay to share code if made available to all participants on the forums.

3. Team Limits and Submission Limits

The maximum team size is 3. The prize will be split between all members of the team evenly, unless team requests otherwise.

You may submit up to 10 entries per day. Only the last one submitted will be used for judging.

4. Eligibility

In order to be eligible to participate in the competition, you must be currently a Carleton University, University of Ottawa, Algonquin College or Collège La Cité student in the 2022 winter semester (January-April) and must be able to provide reasonable proof (I.e. a valid ID and most recent transcript with date is reasonable). In order to be eligible to win a prize, you may be asked to reproduce your scores with your models. So it may be in your best interest to save your models persistently.

5. External Data

You may use data other than the competition data to develop and test your models and Submissions.

6. Use of Open Source

Unless otherwise stated in the specific competition rules above, if open-source code is used in the model to generate the submission, then you must only use open source code licensed under an Open Source Initiative-approved license (see www.opensource.org) that in no event limits commercial use of such code or model containing or depending on such code.

7. Disqualification and Rule Changes

CAIS executives reserve the right to disqualify any participant from the competition if it is reasonably believed that the participant has attempted to undermine the legitimate operation of the competition by cheating, deception, or other unfair playing practices or abuses, threatens or harasses any other participants. CAIS executives reserve the right to add or modify any rules listed above. Participants will be notified when any addition or modification has been made to the competition.

Check out past events!