Kernel regression

Formal sciences
Statistics
Statistical inference
Non-parametric statistics

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Related lectures (32)

Covers nonparametric statistics, kernel density estimation, Bayesian principles, and posterior distribution summarization.

Data Representations: Learning Methods

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

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Neural Network: Random Features and Kernel Regression

Covers random features in neural networks and kernel regression equivalence.

Applying the learning bound to kernel regression

Discusses the application of the main theorem to least square regression in a RKHS, focusing on LR of the Rademacher bound and Lipschitz constant.

Nonparametric Regression: Local Polynomial Estimation

Explores nonparametric regression using local polynomial estimation to balance data fidelity and smoothness.

Dimensionality Reduction: PCA and LDA

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

Kernel Methods: Machine Learning

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

Polynomial Networks: Spectral Bias Analysis

Delves into the spectral bias of polynomial neural networks, analyzing the impact on learning different frequencies and discussing experimental results.

Feature Expansion: Kernels and KNN

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

Machine Learning in Molecular Dynamics

Explores the application of machine learning in molecular dynamics and materials, emphasizing the creation of meaningful features and the importance of generalizability.

Feature Selection, Kernel Regression, Neural Networks Playground

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

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