Understanding ImageNet Classifiers

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Explores the evaluation of machine and human accuracy and robustness on ImageNet, highlighting progress, challenges, and the need for improvement.

Examines the generalization of ImageNet classifiers, safety-critical applications, overfitting, and the reliability of machine learning models.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Explores robustness in visual intelligence, addressing distribution shifts, failure examples, and strategies for improvement.

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.