Nonparametric Regression: Kernel-Based Estimation

Related lectures (31)

Page 3 of 4

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Explores feature expansion, kernel methods, SVM, and nonlinear classification in machine learning.

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

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

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

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

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