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 marketing2) Marketing data collection and management for machine learning approaches3) Supervised learning fundamentals4) Unsupervised learning fundamentals5) Recommender systems6) Introduction to deep learning7) Computer Vision and natural language processing8) 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. Direct link to OLAT course: https://lms.uzh.ch/url/repositoryentry/17250386164
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