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Montag, 11. November 2019
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VHB Gruppe

  

Kernaktivitäten

 

82. Jahrestagung des VHB

17. bis 20. März 2020

in Frankfurt am Main

 

 

 

Wissenschaftliche Kommissionen

2004MS01

Machine Learning

The course exposes participants to recent developments in the field of machine learning and discusses their ramifications for business and economics. Machine learning comprises theories, concepts, and algorithms to infer patters from observational data. The prevalence of data (“big data”) have led to an increasing interest in corresponding methodology to leverage existing data assets for improved decision-making and business process optimization. Concepts such as business analytics, data science, and artificial intelligence are omnipresent and ground to a large extend on machine learning. Familiarizing course participants with these concepts and enabling them to purposefully apply cutting-edge methods to real-world decision problems in management, policy development, and research is the overarching objective of the course. Accordingly, the course targets PhD students and young researchers who want to employ machine learning in their research. A clear and approachable explanation of relevant methodologies and recent developments in machine learning paired with a batterie of practical exercises using contemporary software libraries of (deep) machine learning will ready participants for design-science or empirical-quantitative research projects.

 

 

 

Date of Event:

06. - 09. April 2020

Location:

Harnack-Haus
Ihnestraße 16-20
14195 Berlin

 

Course Language

English

 

Lecturer:

 

 

Prof. Dr. Stefan Lessmann
Humboldt-Universität zu Berlin

Registration:

 

To get an overview of the amount of the participation fee and to register for the course, please use this link: https://vhbonline.org/veranstaltungen/prodok/anmeldung/ (https://vhbonline.org/en/events/the-phd-program/registration-participation-payment/)

 

You can also send an email to prodok(at)vhbonline(dot)org.

 

Registration Deadline: 1. March 2020

 
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