Clog Loss: Advance Alzheimer’s Research with Stall Catchers Hosted By MathWorks


Woohoo! This competition has come to a close!

Many thanks to the participants for all of their hard work and commitment to using data for good!


MathWorks, makers of MATLAB and Simulink software, is sponsoring this challenge and the bonus award for Top MATLAB user. They're also supporting participants by providing complimentary software licenses and learning resources.

To request your complimentary license, go to the MathWorks site, click the “Request Software” button, and fill out the software request form.

MATLAB code to help you get started with the competition dataset is available on GitHub along with a walkthrough blog post.

During the course of the competition, the MathWorks Student Competition team is also available to support participants. MathWorks Student Competition team supports competitions from all over the world in the areas such as aerospace, automotive, AI & robotics, among others. Neha Goel, Technical Lead for Data Science & Deep Learning Competitions, will be your point of contact. You can learn more about 5 different methods of video classification to consider using in this competition by checking out her blog.

If you have questions, email MathWorks at or post on the DrivenData community forum.

Additional resources

Below are some links to learn about MATLAB, Deep Learning Toolbox, and other useful resources for this competition.


  1. MATLAB Onramp
  2. Deep Learning Onramp
  3. Computer Vision Video Tutorials

General Resources

  1. Deep Network Designer App
  2. 8 MATLAB Cheat Sheets for Data Science
  3. Getting Started with Datastore
  4. Define Custom Deep Learning Layers
  5. Specify Layers of Convolutional Neural Network
  6. Pretrained Deep Neural Networks


  1. Classify Videos Using Deep Learning
  2. 3-D Brain Tumor Segmentation Using Deep Learning
  3. Deep Learning with Images
  4. Image Processing Toolbox - Examples


  1. Machine Learning with MATLAB: Getting Started with Classification
  2. Deep Learning with MATLAB