Generalization error

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Related lectures (31)

Model Assessment and Hyperparameter Tuning

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

Gradient Descent Methods for Artificial Neural Networks

Explores gradient descent methods for training artificial neural networks, covering supervised learning, single-layer networks, and modern gradient descent rules.

Double Descent Curves: Overparametrization

Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.

Evaluating Machine Accuracy and Robustness on ImageNet

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

Receiver-Operator Characteristics: ROC Curves

Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Generalization and Overfitting

Covers generalization, overfitting, and model complexity in machine learning.

Error Decomposition and Regression Methods

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

Polynomial Regression: Overview

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

Data Representations & Processing

Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

Regression Trees and Ensemble Methods in Machine Learning

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

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