Machine Learning for Signal Processing

In this competition, you are tasked with developing a controller to enable a physiologically-based human model with a prosthetic leg to walk and run. You are provided with a human musculoskeletal model, a physics-based simulation environment OpenSim where you can synthesize physically and physiologically accurate motion, and datasets of normal gait kinematics. You are scored based on how well your agent adapts to the requested velocity vector changing in real time.

In many real-world machine learning applications, AutoML is strongly needed due to the limited machine learning expertise of developers. Moreover, batches of data in many real-world applications may be arriving daily, weekly, monthly, or yearly, for instance, and the data distributions are changing relatively slowly over time. This presents a continuous learning or Lifelong Machine Learning challenge for an AutoML system.

In this competition you can take on the role of an attacker or a defender (or both). As a defender you are trying to build a visual object classifier that is as robust to image perturbations as possible. As an attacker, your task is to find the smallest possible image perturbations that will fool a classifier.