Statistical learning theory

Related lectures (31)

Page 3 of 4

Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.

Explores overfitting and accuracy measures in image classification, emphasizing the importance of model generalization and optimal accuracy.

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.

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

Delves into machine learning's impact on privacy, discussing attacks, vulnerabilities, and ethical considerations in data usage.

Covers loading datasets, understanding dimensions, learning curves, and the impact of regularization on overfitting.

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