Want to know what linear algebra, signal compression, machine learning and MRI have in common?
In this episode, our guest speaker Professor Simone Brugiapaglia introduces the mathematics of data science. In particular, he discusses two major branches of the field, namely sparse recovery and compressed sensing, which are inherent in his research. He elaborates on applications of sparse recovery, on the Deep Neural Networks, a subset of machine learning, and on how they are related to data science. To conclude the episode, he offers insightful advice for students interested in research or in applying the knowledge to the industry.
Links to the resources mentioned in the Podcast:
https://m.youtube.com/channel/UCm5mt-A4w61lknZ9lCsZtBw (mobile link only)