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Machine Learning in Marketing - Theoretical Foundations and Applications - Detailseite

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Grunddaten
Veranstaltungsart Vorlesung/Übung Veranstaltungsnummer 707101
Semester WiSe 2020/21 SWS 4
Rhythmus keine Übernahme Moodle-Link  
Veranstaltungsstatus Freigegeben für Vorlesungsverzeichnis  Freigegeben  Sprache englisch
Belegungsfrist Es findet keine Online-Belegung über AGNES statt!
Veranstaltungsformat Digital

Termine

Gruppe 1
Tag Zeit Rhythmus Dauer Raum Raum-
plan
Lehrperson Status Bemerkung fällt aus am Max. Teilnehmer
Mo. 17:00 bis 20:00 s.t. wöch von 09.11.2020  Institutsgebäude - 220 Spandauer Straße 1 (SPA 1) - (Hör-/Lehrsäle ansteigend mit Experimentierbühne)   findet statt    
Gruppe 1:
 


Zugeordnete Person
Zugeordnete Person Zuständigkeit
Gabel, Sebastian , Dr.
Studiengänge
Abschluss Studiengang LP Semester
Master of Science  Betriebswirtschaftslehre Hauptfach ( Vertiefung: kein LA; POVersion: 2016 )     -  
Programmstud.-o.Abschl.MA  Betriebswirtschaftslehre Programm ( POVersion: 1999 )     -  
Master of Science  Economics/ Management Sc. Hauptfach ( Vertiefung: kein LA; POVersion: 2016 )     -  
Programmstud.-o.Abschl.MA  Statistik Programm ( POVersion: 1999 )     -  
Master of Science  Volkswirtschaftslehre Hauptfach ( Vertiefung: kein LA; POVersion: 2016 )     -  
Programmstud.-o.Abschl.MA  Volkswirtschaftslehre Programm ( POVersion: 1999 )     -  
Master of Science  Wirtschaftsinformatik Hauptfach ( Vertiefung: kein LA; POVersion: 2016 )     -  
Programmstud.-o.Abschl.MA  Wirtschaftsinformatik Programm ( POVersion: 1999 )     -  
Master of Education (BS)  Wirtschaftspädagogik (WV) 1. Fach ( Vertiefung: mit LA-Option; POVersion: 2015 )     -  
Programmstud.-o.Abschl.MA  Wirtschaftspädagogik (WV) Programm ( POVersion: 1999 )     -  
Zuordnung zu Einrichtungen
Einrichtung
Wirtschaftswissenschaftliche Fakultät, Marketing
Inhalt
Kommentar

Course Prerequisites: No prerequisites, but successful participation in at least one of the following statistics/data science courses isrecommended: Advanced Marketing Modeling, Selected Topics in Statistical and Machine Learning, Business Analytics & Data Science, Advanced Data Analytics for Management Support. In addition, students should be proficient in Python and have a good understanding of statistics, probability theory and linear algebra.

Description and Objectives: This course is designed for master students from quantitative fields such as marketing, economics, statistics and computer science in their last year of study.  It prepares students for solving real-world marketing problems using modern quantitative methods and is a good preparation for a machine learning/data science job in marketing or a PhD in quantitative marketing.

To this end, the course first reviews theoretical foundations in marketing, statistics, probability theory and computer science that are required to understand, apply and customize complex statistical models.  The course will then focus on formalizing marketing decisions as machine learning problems and equips students with the necessary tools to efficiently implement machine learning models and pipelines.  After completing this course participants will be able to judge how modern machine learning methods complement (or even replace) traditional statistical methods for data analysis and decision-making.

The course content complements existing courses in that it reviews the theoretical foundations taught in statistics and computer science programs and then shows how to implement machine learning approaches to important marketing questions.

Course material will be made available in the Moodle system of the Humboldt-University Berlin.

Grades are solely based on the final (written) exam.  Successful participation in the home assignments serves as a preparation for the exam and is not mandatory for admission.  Reference solutions to the home assignments will be discussed in the exercises.

All lectures and exercises are based on Python.  The students will use popular data and machine learning libraries including (among others) numpy, scipy, pandas, scikit-learn, pytorch, lightgbm and statsmodels.  Students can run Python via jupyterlab or in their preferred IDE.  It is strongly recommended to manage libraries using virtualenv.  The sample code is tested on Unix and students are encouraged to use a Unix OS (Mac OS X or Linux).  Support for Windows is not guaranteed.

Literatur

Bishop, C.M., 2006. Pattern Recognition and Machine Learning. Springer.

Friedman, J., Hastie, T. and Tibshirani, R., 2001. The Elements of Statistical Learning (Vol. 1, No. 10). New York: Springer Series in Statistics.

Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep Learning. MIT press.

Murphy, K.P., 2012. Machine Learning: A Probabilistic Perspective. MIT press.

Bemerkung

StO/PO MA 2016: 6 LP, Modul: "Selected Topics in Business Administration"

Prüfung

Written exam (90 min)

Strukturbaum

Die Veranstaltung wurde 6 mal im Vorlesungsverzeichnis WiSe 2020/21 gefunden:

Humboldt-Universität zu Berlin | Unter den Linden 6 | D-10099 Berlin