47-843 Foundations of Data Science

6 units, Carnegie Mellon University

Modern data in many Machine Learning applications, Recommendation Systems, Neural nets, Ranking, as well as other areas consists of vectors with many components. The abstraction of data to high dimensional vectors is not just a bookkeeping device, but is fundamental to our understanding of the structure, properties and uses of the data. There are three types of lenses with which properties of a large collection of high dimensional vectors can be studied and understood Geometric, Linear Algebraic and Statistical. The purpose of the course is to introduce each of these three. While applications will motivate our study, the focus is on mathematical properties and not specific applications.

Prerequisites: none listed

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