Syllabus

Title
5808 Statistical Learning
Instructors
Assoz.Prof. PD Dr. Bettina Grün
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/15/22 to 02/26/22
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Wednesday 03/02/22 02:30 PM - 04:30 PM D4.0.019
Wednesday 03/09/22 02:30 PM - 04:30 PM TC.3.12
Wednesday 03/16/22 02:30 PM - 04:30 PM D4.0.127
Wednesday 03/23/22 02:30 PM - 04:30 PM D4.0.127
Wednesday 03/30/22 02:30 PM - 04:30 PM D4.0.127
Wednesday 04/06/22 02:30 PM - 04:30 PM D4.0.019
Wednesday 04/27/22 02:30 PM - 04:30 PM D4.0.019
Wednesday 05/04/22 02:30 PM - 04:30 PM D4.0.127
Wednesday 05/11/22 02:30 PM - 04:30 PM TC.3.07
Wednesday 05/18/22 02:30 PM - 04:30 PM D4.0.127
Wednesday 05/25/22 02:30 PM - 04:30 PM D4.0.039
Wednesday 06/01/22 02:30 PM - 04:30 PM D4.0.019
Wednesday 06/08/22 02:30 PM - 04:30 PM D4.0.133
Wednesday 06/15/22 02:30 PM - 04:30 PM TC.3.12
Contents

The course will cover topics in statistical learning including:

  • 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.
Learning outcomes

Students know about advantages and pitfalls of various statistical learning methods as well as their application and tuning and are able to apply selected methods.

Attendance requirements

For this course participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).

Teaching/learning method(s)

This course is taught as lectures and tutorials combined with assignments and a project. In combination with the lectures, the assignments and the project help students to consolidate and expand their understanding of the methods discussed in the lectures.

Assessment

Grading is based on assignments (30%), a project (30%) and a presentation (40%).

Last edited: 2021-10-28



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