Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.
Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
