Concept
Statistical learning theory
Related lectures (53)
Mathematics of Data: Models and Estimators
Covers the Mathematics of Data, focusing on models, estimators, and practical issues in data analysis.
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.
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.
Metrics for Classification
Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.
Data Representations & Processing
Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.
Addressing Overfitting in Decision Trees
Explores overfitting in decision trees and introduces random forests as a solution.
SVM Hyperparameters
Delves into SVM hyperparameters, showcasing the impact of C and kernel width on classification results.
Optimal Regularization Strength and Learning Curves
Covers loading datasets, understanding dimensions, learning curves, and the impact of regularization on overfitting.