• Brief introduction to Statistical Learning: Regression versus Classification; Linear Regression: simple and multiple Linear Regression; Classification: Logistic Regression, Discriminant Analysis; Resampling Methods: Cross-Validation, the Bootstrap; Regularization: Subset Selection, Ridge Regression, the Lasso, Principle Components and Partial Least Squares Regression; Nonlinear Models: Polynomial; Splines; Generalized Additive Models; Tree-Based Models: Decision Trees, Random Forest, Boosting; Support Vector Machines; Unsupervised Learning: Principle Component Analysis, Clustering Methods.

    For further information see the academic catalog: IAM557

  • \( \LaTeX \) and Matlab; Basic Commands and Syntax of \( \LaTeX \) and Matlab; Working within a Research Group via Subversion; Arrays and Matrices; Scripts and Function in Matlab; Commands and Environments in \( \LaTeX \); More on Matlab Functions; Toolboxes of Matlab; Packages in \( \LaTeX \); Graphics in Matlab; Handling Graphics and Plotting in \( \LaTeX \); Advanced Techniques in Matlab: memory allocation, vectoristaion, object orientation, scoping, structures, strings, file streams.

    For further information see the academic catalog: IAM591