Kernel Methods: Understanding Overfitting and Model Selection

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Kernel Methods in Machine Learning: Kernel Regression and SVM

Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.

Probabilistic Linear Regression

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

Supervised Learning in Financial Econometrics

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Kernel Methods

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

Cross-validation & Regularization

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Linear Regression

Covers the concept of linear regression, including polynomial regression and hyperparameters selection.

Linear and Ridge Regression

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

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Linear Systems: Modeling and Identification

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.