Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Prediction, Generalization, and Complexity in Statistical Decision Theory - Part 2

Simons Institute via YouTube

Overview

Explore the second part of a lecture that delves into the classical statistical decision theory's approach to prediction error, generalization gap, and model complexity. Examine the fixed-X perspective in statistics and its limitations when applied to machine learning's random-X setting. Discover how classical statistical concepts can be reinterpreted and extended to accommodate the random-X framework, particularly in cases where predictive models interpolate training data. Gain insights into the differences between statistical and machine learning approaches to generalization and model complexity, and learn how these perspectives can be reconciled for a more comprehensive understanding of predictive modeling.

Syllabus

Prediction, Generalization, Complexity: Revisiting the Classical View from Statistics Part 2

Taught by

Simons Institute

Reviews

Start your review of Prediction, Generalization, and Complexity in Statistical Decision Theory - Part 2

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.