Nonparametric regression

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Related lectures (32)

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Kernel Regression: K-nearest Neighbors

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Neural Network: Random Features and Kernel Regression

Covers random features in neural networks and kernel regression equivalence.

Non-parametric Regression: Smoothing Techniques

Explores non-parametric regression techniques, including splines, bias-variance tradeoff, orthogonal functions, wavelets, and modulation estimators.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Decision Trees and Random Forests: Concepts and Applications

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Decision Trees: Regression and Classification

Covers decision trees for regression and classification, explaining tree construction, feature selection, and criteria for induction.

Generalized Linear Models II: GLM Extensions

Covers advanced topics in Generalized Linear Models, focusing on link functions, error distributions, and model interpretation.

Data Representations: Learning Methods

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

Gaussian Process Regression: Probabilistic Linear Regression

Explores Probabilistic Linear Regression and Gaussian Process Regression, emphasizing kernel selection and hyperparameter tuning for accurate predictions.

Regression Models: Performance and Evaluation

Explores regression model performance, learning errors, and building regression trees using the CART algorithm.