Lectures related to Understanding ImageNet Classifiers

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.

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Covers the design and optimization of neural network models in deep learning.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.