An introduction to modern statistical and machine learning with applications in R. Topics to be covered include

• regularized regression (lasso and elastic net),
• model assessment and selection (cross-validation and bootstrap),
• regression and classification trees,
• ensemble methods (bagging, random forests, boosting),
• deep learning with neural networks.

After completing this course the student will have the ability to:

• describe and apply key methods of statistical and machine learning including regularized regression, regression and classification trees, ensemble methods and deep learning with neural networks;
• interpret estimation results and perform model assessment and selection, e.g., using cross-validation and bootstrapping.

Moreover, after completing this course the student will have the ability to:

• adequately communicate the results of fitting a suitable statistical and machine learning model to data;
• critically assess the application of statistical and machine learning methods for data analysis.

In addition, the student will be able to:

• use R to perform data analysis using statistical and machine learning methods.

Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

The course is taught as a lecture combined with homework assignments and a course project.

In combination with the lecture, the assignments will help students to consolidate and expand their knowledge and understanding of the statistical and machine learning methods covered in class as well as model assessment and selection approaches by developing solutions to theoretical and applied problems.

The course project will help students to gain some experience in applied data analysis using statistical and machine learning methods by applying several statistical and machine learning methods in combination with model assessment and selection methods to arrive at suitable model. Students will work on the course project in groups and present their results in class.

The assessment is based on three evaluation criteria with the following weights:

• 30% assignments
• 30% course project
• 40% written final exam

Grading key: Unsatisfactory: <50%; Adequate: <65%; Satisfactory: <80%; Good: <90%; Very good: <=100%.

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