Feature Expansion: Kernels and KNN

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Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Data Representations: Learning Methods

Covers polynomial feature expansion, kernel functions, regression, and SVM, emphasizing the importance of choosing functions for feature expansion.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Feature Selection, Kernel Regression, Neural Networks Playground

Covers feature selection, kernel regression, and neural networks through exercises.

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.

Kernel Methods: Understanding Overfitting and Model Selection

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

Nonparametric Regression: Kernel-Based Estimation

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Neural Networks: Random Features and Kernel Regression

Explores random features in neural networks and kernel regression using stochastic gradient descent.

Kernel Regression: Basics and Applications

Explores kernel regression, the curse of dimensionality, and random features in neural networks.

Kernel Methods: Machine Learning

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.