Linear and Weighted Regression: Solution to Exercises

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

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

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

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

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