Statistical Learning

Statistical learning, multiple linear regression, classification, resampling methods, modell selection/regularization, non-linearity, tree-based methods, neural networks.

1. Knowledge: The student has knowledge about the most popular statistical models and methods that are used for prediction in science and technology, with emphasis on regression- of classification-type statistical models.

2. Skills: The student can, based on an existing data set, choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software. The student can present, interpret and communicate the results from the statistical analyses, and knows which conclusions can be drawn from the analyses, and what are the caveats.

Lectures, exercises and compulsory works (projects). The assessment is a final written examination (100%). More information about the compulsory project will be give at semester start.