Concept

Statistical learning theory

Related lectures (31)

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

Do ImageNet Classifiers Generalize?

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

Mathematics of Data: Models and Estimators

Covers the Mathematics of Data, focusing on models, estimators, and practical issues in data analysis.

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.

Linear Models and Overfitting

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.

Regularization Methods: Training and Validation Base

Explores regularization methods in neural networks, emphasizing the importance of training and validation bases to prevent overfitting.

Specification Testing and Machine Learning

Explores specification testing, machine learning, overfitting, regularization, prediction tests, and variable selection.

Regression Trees and Ensemble Methods in Machine Learning

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

Bias-Variance Trade-Off

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

Machine Learning Fundamentals: Overfitting and Regularization

Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.

Neural Networks: Regularization & Optimization

Explores neural network regularization, optimization, and practical implementation tips.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Generalization in Deep Learning

Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.

Bayesian Statistics: Regularization and Divergence

Covers Kullback-Leibler divergence, regularization, and Bayesian statistics to combat overfitting in machine learning models.

Learning with Deep Neural Networks

Explores the success and challenges of deep learning, including overfitting, generalization, and the impact on various domains.

Overfitting: Symptoms and Characteristics

Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

Metrics for Classification

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.

Data Representation: BoW and Imbalanced Data

Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.

Cross-validation & Regularization

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Model Assessment and Hyperparameter Tuning

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