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Course

MATH-412: Statistical machine learning

Summary

A course on statistical machine learning for supervised and unsupervised learning

Lectures

Instructors (2)

Guillaume Romain Obozinski

Guillaume Obozinski is Deputy Chief Data Scientist at the Swiss Data Science Center. He graduated with a PhD in Statistics from UC Berkeley in 2009. He did his postdoc and held until 2012 a researcher position in the Willow and Sierra teams at INRIA and Ecole Normale Supérieure in Paris. He was then Research Faculty at Ecole des Ponts ParisTech until 2018. Guillaume has broad interests in statistics and machine learning and worked over time on sparse modeling, optimization for large scale learning, graphical models, relational learning and semantic embeddings, with applications in various domains from computational biology to computer vision.

Yoav Zemel

Official source

https://edu.epfl.ch/coursebook/en/statistical-machine-learning-MATH-412

Moodle Page

http://go.epfl.ch/MATH-412

About this result

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Summary

A course on statistical machine learning for supervised and unsupervised learning

Lectures

Instructors (2)

Guillaume Romain Obozinski

Yoav Zemel

Official source

https://edu.epfl.ch/coursebook/en/statistical-machine-learning-MATH-412

Moodle Page

http://go.epfl.ch/MATH-412

About this result

Lectures in this course (49)

Introduction to Supervised Learning and Decision Theory

Covers supervised learning, decision theory, risk minimization, and goal achievement.

Examples of decision models for Supervised learning

Covers examples of decision models for supervised learning, including regression, classification, ranking pairs, and sequence decoding for OCR models.

Comparing L1 and L0 + Greedy algorithms

Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.

Risk Estimation: Mallows' CL and Cp

Discusses optimism in risk estimation, effective degrees of freedom, and Mallows' CL and Cp for linear estimators.

The Information Criteria of Takeuchi and Akaike

Covers the estimation of optimism for predictors via statistical models and the application of AIC to linear regression.

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