Sparse Representations in Computer Vision and Machine Learning

Course Description

Sparse and redundant representations constitute a fascinating area of research in signal and image processing. This is a relatively young field that has been taking form for the last 15 years or so, with contributions from harmonic analysis, numerical algorithms and machine learning, and has been vastly applied to myriad of problems in computer vision and other domains. This course will focus on sparsity as a model for general data, generalizing many different other constructions or priors. This idea - that signals can be represented with just a few coefficients - leads to a long series of beautiful (and surprisingly, solvable) theoretical and numerical problems, and many applications that can benefit directly from the new developed theory. In this course we will survey this field, starting with the theoretical foundations and systematically covering the knowledge that has been gathered in the past years. This course will touch on theory, numerical algorithms, and applications in image processing and machine learning. Prerequisite(s): Mathematical Methods or equivalent

Code

EN.585.719

Credits

3

Subject

Applied Biomedical Engineering

Programs That Include This Course

• Master's Applied and Computational Mathematics, Master of Science
• Master's Applied Biomedical Engineering, Master of Science