Course

MATH-413: Statistics for data science

Summary

Statistics lies at the foundation of data science, providing a unifying theoretical and methodological backbone for the diverse tasks enountered in this emerging field. This course rigorously develops the key notions and methods of statistics, with an emphasis on concepts rather than techniques.

Lectures

Instructors (2)

Myrto Limnios

Teaching & PhD Courses Empirical processes MATH-522 We learn how to control the nonasymptotic and random behavior of collections of estimators, when indexed by classes of functions/sets. Examples range from prototypical estimators used by statisticians, to nonparametric models used in machine learning. Regression methods MATH-408 General graduate course on regression methods

Rajita Ramesh Chandak

Expertise Nonparametric estimation, statistical inference, theory of machine learning Teaching & PhD Courses Nonparametric estimation and inference MATH-524 Nonparametric models are used to identify a wide range of relationships within data. This course gives a graduate-level overview of nonparametric statistical estimation and inference theory. Statistical inference MATH-562 Inference from the particular to the general based on probability models is central to the statistica

Official source

https://edu.epfl.ch/coursebook/en/statistics-for-data-science-MATH-413

Moodle Page

http://go.epfl.ch/MATH-413

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Summary

Lectures

Instructors (2)

Myrto Limnios

Rajita Ramesh Chandak

Official source

https://edu.epfl.ch/coursebook/en/statistics-for-data-science-MATH-413

Moodle Page

http://go.epfl.ch/MATH-413

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