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Course

COM-406: Foundations of Data Science

Summary

We discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas and techniques that come from probability, information theory as well as signal processing.

Lectures

Instructor

Michael Christoph Gastpar

Michael Gastpar is a (full) Professor at EPFL. From 2003 to 2011, he was a professor at the University of California at Berkeley, earning his tenure in 2008. He received his Dipl. El.-Ing. degree from ETH Zürich, Switzerland, in 1997 and his MS degree from the University of Illinois at Urbana-Champaign, IL, USA, in 1999. He defended his doctoral thesis at EPFL on Santa Claus day, 2002. He was also a (full) Professor at Delft University of Technology, The Netherlands. His research interests are in network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. He is a Fellow of the IEEE. He is the co-recipient of the 2013 Communications Society & Information Theory Society Joint Paper Award. He was an Information Theory Society Distinguished Lecturer (2009-2011). He won an ERC Starting Grant in 2010, an Okawa Foundation Research Grant in 2008, an NSF CAREER award in 2004, and the 2002 EPFL Best Thesis Award. He has served as an Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11), and as Technical Program Committee Co-Chair for the 2010 International Symposium on Information Theory, Austin, TX.

Official source

https://edu.epfl.ch/coursebook/en/foundations-of-data-science-COM-406

Moodle Page

http://go.epfl.ch/COM-406

Summary

Lectures

Instructor

Michael Christoph Gastpar

Official source

https://edu.epfl.ch/coursebook/en/foundations-of-data-science-COM-406

Moodle Page

http://go.epfl.ch/COM-406

Lectures in this course (89)

Introduction: Foundations of Data Science

Covers distribution estimation, property testing, and multi-arm bandits in data science.

Review of Probability

Covers the review of probability concepts including Poisson distribution and moment generating functions.

Signal Representations

Covers signal representations in Hilbert Spaces and the importance of orthogonality.

Signal Representations

Explores signal representations, including Fourier transform, time-frequency analysis, and multi-resolution analysis using window functions like Gaussians.

Signal Representations

Explores wavelets as orthonormal bases for piecewise constant signals over unit intervals.

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