Display language
To modulepage Generate PDF

#70382 / #2

SoSe 2020 - WiSe 2021/22

English

Econometrics and Machine Learning

6

Andresen, Anna

benotet

Schriftliche Prüfung

Zugehörigkeit


Fakultät VII

Institut für Volkswirtschaftslehre und Wirtschaftsrecht

37312400 FG Digitale Währungen / Kryptowährungen

Volkswirtschaftslehre

Kontakt


EN 17

Andresen, Anna

anna.almosova@tu-berlin.de

No information

Learning Outcomes

The module focuses on machine learning methods through the paradigm of economic thinking and highlight similarities and differences between classical econometrics and machine learning. The students get a thorough overview and hands-on experience with modern machine learning methods and their possible economic applications. They obtain solid basic knowledge of current machine learning techniques and are able to apply them to real-life problems. They get experienced with programming in Python and using the state of-the-art machine learning libraries.

Content

Lectures that describe theoretical background of different statistical and machine learning techniques: classification and regression trees, LASSO, k-nearest-neighbors, neural networks, support vectors machines etc. as well as general principles: cross validation, out-of-sample accuracy, concepts of probability theory. Tutorials deal with real life economic problems and demonstrate how to apply machine learning techniques to these problems using Python programming language.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Machine Learning Methods for EconomistsIV3731 L 10560SoSeEnglish4

Workload and Credit Points

Machine Learning Methods for Economists (IV):

Workload descriptionMultiplierHoursTotal
Attendance15.04.0h60.0h
Pre/post processing15.08.0h120.0h
180.0h(~6 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

Lectures with theoretical background and examples; tutorials with programming exercises; homework assignments for students that might include both theoretical/mathematical questions and programming tasks.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Basic knowledge of statistics, mathematics and econometrics. Basic knowledge of any programming language is desirable but not required. Bachelor students may attend the course.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Written exam

Language

English

Duration/Extent

120 minutes

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Sommersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Participation in this module requires no registration.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
No recommended literature given

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
This module is not used in any degree program.

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

No information