Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Concept

Statistical learning theory

Related lectures (31)

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

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

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

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

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

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

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

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

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

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

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

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

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

Explores the impact of model complexity on error rates in machine learning.

Explores overfitting in decision trees and introduces random forests as a solution.

Delves into SVM hyperparameters, showcasing the impact of C and kernel width on classification results.

Discusses the concept of data dredging in machine learning and its risks.

Covers quantifying performance through true positives, false negatives, and false positives in machine learning.

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

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