Lectures related to Singular Value Decomposition: Theory and Applications

Explains the method of least squares in linear regression to find the best-fitting line to a set of data points.

Explores B-splines, natural cubic splines, and smoothing splines in regression problems and their practical applications.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

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

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