040348 UK Statistical Learning & Analytics (MA) (2025S)

6.00 ECTS (3.00 SWS), SPL 4 - Wirtschaftswissenschaften

Continuous assessment of course work

Moodle

Th 15.05. 09:45-13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Registration is open from Mo 10.02.2025 09:00 to Tu 18.02.2025 12:00
Registration is open from We 26.02.2025 09:00 to Th 27.02.2025 12:00
Deregistration possible until Fr 14.03.2025 23:59

Details

max. 50 participants

Language: English

Lecturers

Wenjie Tang

Classes (iCal) - next class is marked with N

Thursday 27.03. 09:45 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 03.04. 09:45 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 10.04. 09:45 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
N Thursday 15.05. 09:45 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 22.05. 09:45 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 05.06. 09:45 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 12.06. 09:45 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 26.06. 09:45 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

This course aims to provide a holistic overview of the modern Statistical Learning toolbox. This course emphasizes on understanding the intuition behind the tools and not on deriving the underlying mathematics. We will make productive use of analytics tools available in Python. While the class focuses on simplified models, it aims to bridge the classroom knowledge and business applications. Topics will include linear regression, generalized linear regression, resampling methods, model selection, regularization, etc.

Assessment and permitted materials

Class participation (Individual) 20%
Assignment (Group) 20%
In-class quiz (Individual) 40%
Final Project (Group) 20%

Minimum requirements and assessment criteria

Requirements to successfully complete the class: at least 50% from the following four different types of required work

Class participation (Individual) 20%
Assignment (Group) 20%
In-class quiz (Individual) 40%
Final Project (Group) 20%

Examination topics

Class participation, assignment, in-class quiz, final project.

Reading list

Association in the course directory

Last modified: Mo 03.03.2025 15:25