Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
