Kernel Regression

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Related lectures (31)

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Feature Expansion: Kernels and KNN

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

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.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Kernel Regression: Weighted Average and Feature Maps

Covers kernel regression and feature maps for data separability.

Clustering & Density Estimation

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

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.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Unsupervised Learning: Dimensionality Reduction

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.