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Related lectures (32)
Probabilistic Models for Linear Regression
Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.
Linear Regression: Mean-square-error Inference
Covers the MSE problem in linear regression models, focusing on the optimal estimator and data fusion methods.
The Bacterial Flagellar Engine: Automatic Gearshift, Efficient Model Discovery
Examines the bacterial flagellar engine's automatic gearshift mechanism and efficient model discovery in response to changing loads.
Modern Regression: Overdispersion and Model Assessment
Explores overdispersion, model assessment, and regression techniques for count data.
Data-Driven Modeling: Regression
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Kernel Regression: Weighted Average and Feature Maps
Covers kernel regression and feature maps for data separability.
Linear Regression Basics
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Weighted Least Squares Estimation: IRLS Algorithm
Explores the IRLS algorithm for weighted least squares estimation in GLM.
Multicollinearity: Dangers and Remedies
Explores the dangers of multicollinearity in linear models and discusses diagnostic methods and remedies.
Generalized Linear Regression: Classification
Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.