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Machine Learning in Marketing


Dozent/in Prof. Dr. Marc Pouly
Veranstaltungsart Vorlesung
Code HS221212
Semester Herbstsemester 2022
Durchführender Fachbereich Wirtschaftswissenschaften
Studienstufe Master
Termin/e Di, 20.09.2022, 10:15 - 12:00 Uhr, 4.B46
Di, 27.09.2022, 10:15 - 12:00 Uhr, HS 12
Di, 27.09.2022, 08:15 - 10:00 Uhr, HS 12
Di, 04.10.2022, 10:15 - 12:00 Uhr, HS 12
Di, 11.10.2022, 10:15 - 12:00 Uhr, HS 12
Di, 11.10.2022, 08:15 - 10:00 Uhr, HS 12
Di, 18.10.2022, 10:15 - 12:00 Uhr, HS 12
Di, 25.10.2022, 10:15 - 12:00 Uhr, HS 12
Di, 25.10.2022, 08:15 - 10:00 Uhr, HS 12
Di, 08.11.2022, 10:15 - 12:00 Uhr, HS 12
Di, 08.11.2022, 08:15 - 10:00 Uhr, HS 12
Di, 15.11.2022, 10:15 - 12:00 Uhr, HS 12
Di, 22.11.2022, 10:15 - 12:00 Uhr, HS 12
Di, 22.11.2022, 08:15 - 10:00 Uhr, HS 12
Di, 29.11.2022, 10:15 - 12:00 Uhr, HS 12
Di, 06.12.2022, 10:15 - 12:00 Uhr, HS 12
Di, 06.12.2022, 08:15 - 10:00 Uhr, HS 12
Di, 13.12.2022, 10:15 - 11:45 Uhr, HS 12 (Prüfung)
Umfang 3 Semesterwochenstunden
Turnus weekly
Inhalt This course provides an overview of common machine learning approaches with an emphasis on approaches that are of high relevance to marketing research and management. The course contains the following blocks:

1) Introduction to machine learning in marketing
2) Marketing data collection and management for machine learning approaches
3) Supervised learning fundamentals
4) Unsupervised learning fundamentals
5) Recommender systems
6) Introduction to deep learning
7) Computer Vision and natural language processing
8) Generative models

These parts will be thought both conceptually and in the form of hands-on exercises. We will exclusively work with Python throughout this course.
Lernziele Students will get an overview of machine learning approaches and possible applications in marketing management. They should be able to perform their own analysis using Python on specific marketing research questions. Emphasis is put on modern neural network based approaches for processing of large quantities of unstructured data such as images, text, video and audio.
Voraussetzungen Ideally, students have already attended an introductory course in Python (e.g, Python – A non-technical introduction, https://vv.unilu.ch/details?code=HS201008. If not, we strongly recommend working through one of the many free online tutorials that can be found on the web such as

https://www.learnpython.org

Basic programming skills are sufficient. For machine learning related libraries (numpy and pandas) a separate tutorial will be made available. Prior experience in machine learning is not required. Students should have attended fundamental courses in statistics.
Sprache Englisch
Anmeldung To attend the course / exercise, registration via e-learning platform OLAT is required. Registration is possible from 5 – 30 September 2022. The students themselves are responsible for checking the creditability of the course to their course of study.
Prüfung ***IMPORTANT*** In order to acquire credits, resp. to take the examination, registration via the Uni Portal within the examination registration period is ESSENTIALLY REQUIRED. Further information on registration: www.unilu.ch/wf/pruefungen
Abschlussform / Credits Written examination / 6 Credits
Hörer-/innen Nach Vereinbarung
Kontakt marc.pouly@doz.unilu.ch
benedikt.marxer@unilu.ch