Course

EE-209: Elements of statistics for data science

Summary

Théorie de l'estimation: estimateurs du maximum de vraisemblance, information de Fisher, inégalité de Cramer-Rao, intervalles de confiance.

Tests d'hypothèses: cadre de Neyman-Pearson: test du rapport de vraisemblance, tests paramétriques et non-parmétrique.

Inférence bayésienne

Modèles linéaires

Lectures

Instructor

Philippe Müllhaupt

Expertise Nonlinear Control (Geometry and Algebra)Classical DynamicsRoboticsStatistics Mission Provide rigorous technical and scientific training in nonlinear control, classical mechanics, and statistical theory Current Work Research and teaching in the fields of control theory, statistics, dynamical systems, and general mechanics. Philippe Mullhaupt received his Diplôme d Education Ingï¿½nieur | Electricitï¿½ 1993 – 1993 EPFL DEA | Automatique et Traitement du Signal 199

Official source

https://edu.epfl.ch/coursebook/en/elements-of-statistics-for-data-science-EE-209

Moodle Page

http://go.epfl.ch/EE-209

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Summary

Théorie de l'estimation: estimateurs du maximum de vraisemblance, information de Fisher, inégalité de Cramer-Rao, intervalles de confiance.

Tests d'hypothèses: cadre de Neyman-Pearson: test du rapport de vraisemblance, tests paramétriques et non-parmétrique.

Inférence bayésienne

Modèles linéaires

Lectures

Instructor

Philippe Müllhaupt

Official source

https://edu.epfl.ch/coursebook/en/elements-of-statistics-for-data-science-EE-209

Moodle Page

http://go.epfl.ch/EE-209

About this result

Lectures in this course (24)

Introduction to Continuous Random Variables: Probability Distributions

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Statistics for Data Science: Introduction to Statistical Methods

Covers the fundamental concepts of statistics and their application in data science.

Randomized Trials and Simpson's Paradox

Covers randomized clinical trials, confounding variables, ethical challenges, and Simpson's paradox in data analysis.

Simpson's Paradox: Understanding and Randomized Trials

Explores Simpson's Paradox with a real-world example and emphasizes the significance of randomized trials.

Gaussian Conditional Model and Properties

Explores the Gaussian conditional model for linear regression and the properties of Gaussian data, illustrated with the example of kidney stone treatment comparison.

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