Introduction to Linear Regression

Over fitting and MLE, Effect of Data Size, Training and Test Errors, Regularization and Model Complexity; Linear basis function models, MLE and Least Squares, Geometry of least squares, Sequential Learning, Robust Linear Regression, Ridge Regression, Lasso Regularizer and Sparse Solutions, Multi-output Regression; The Bias-Variance Decomposition.