To achieve full understanding of the use and application of ML algorithms, our participants will work on a real-life industry project, translating theoretical knowledge to practical process and overcoming realistic challenges.
~6 months
~400 work hours total
2-3 students
Real data provided by company
Experienced mentors provided by Y-DATA
Weekly meetings with company data-owner
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Selected Projects 2020


Self-supervised Multi-Person Pose Estimation in Crowded Scenes
Trigo empowers grocery retailers to seamlessly eliminate the industry's leading consumer pain point -- checkout -- through a world-leading computer vision and AI-based system. Accurately predicting human poses is crucial for a smoothly functioning autonomous store. However, annotating data for pose estimation is an extremely expensive endeavor. Thus, improving algorithms by utilizing unused, unlabeled data is of great importance and can make a significant difference, especially if it can be applied in crowded environments. This project takes a novel approach, applying semi-supervised methods to the task of pose estimation. The project’s goal is to use self- and semi-supervised methods and convolutional neural networks to harness unlabeled multi-person images towards improving pose estimation with a limited amount of labeled data.
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Full project cycle

The process of working on the project follows popular industry standards and methodologies and incorporates a growing set of tools the students possess to methodically understand and solve a real-world problem. Our students have a full-cycle data science project in their portfolio upon graduation, covering all industry-standard stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation.