Compressed Sensing Meets Machine Learning: Classification via Sparse Representation and Distributed Pattern Recognition

This Spring, Allen Yang has given a mini course at Berkeley entitled Compressed Sensing Meets Machine Learning. The three lectures are listed here (it includes accompanying code):

• lecture 1: Classification via Sparse Representation
  
• lecture 2: Classification of Mixture Subspace Models via Sparse Representation,
  
• lecture 3: Distributed Pattern Recognition
  

The third lecture is more focused on a very interesting applied subject. I am absolutely sure that it can have some important bearings on similar problems I have covered on in this blog earlier, namely gesture understanding and eye tracking. For certain handicaps the detection is not as important as the continuous quantification of how intervention methods are improving the condition:

• HCI as a way to quantify Autism in infants ? Part I.
• Eye Tracking Machines and Autism: A Business Case ? Part deux
• Eye Tracking Machines and Autism: A Business Case ?