Concept
Overfitting
Related lectures (160)
Generalization and Overfitting
Covers generalization, overfitting, and model complexity in machine learning.
Generalization in Learning with Random Features
Explores generalization in machine learning, focusing on underfitting and overfitting trade-offs, teacher-student frameworks, and the impact of random features on model performance.
Bayesian Statistics: Regularization and Divergence
Covers Kullback-Leibler divergence, regularization, and Bayesian statistics to combat overfitting in machine learning models.
Linear Regression and Logistic Regression
Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Data Representations & Processing
Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.
Generalization Theory
Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.
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.
Gradient Descent: Early Stopping and Stochastic Gradient Descent
Explains gradient descent with early stopping and stochastic gradient descent to optimize model training and prevent overfitting.