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Related lectures (30)

Page 3 of 3

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of model complexity and different cross-validation methods.

Probabilistic Linear Regression

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

Linear and Ridge Regression

Covers linear and ridge regression, overfitting, hyperparameters, and test sets.

Comparing L1 and L0 + Greedy algorithms

Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.

Kernel Methods

Covers overfitting, model selection, validation methods, kernel functions, and SVM concepts.

Polynomial Regression and Gradient Descent

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

Kernel Methods: Understanding Overfitting and Model Selection

Discusses kernel methods, focusing on overfitting, model selection, and kernel functions in machine learning.

Model Selection: Generalization and Validation

Explores generalization, model selection, and validation in machine learning, emphasizing the importance of unbiased model evaluation.

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.

Kernel Methods: Machine Learning

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.