Machine Learning in Marketing

Dozent/in	Prof. Dr. Reto Hofstetter; Prof. Dr. Marc Pouly
Veranstaltungsart	Vorlesung
Code	HS201565
Semester	Herbstsemester 2020
Durchführender Fachbereich	Wirtschaftswissenschaften
Studienstufe	Master
Termin/e	Di, 15.09.2020, 10:15 - 12:00 Uhr, ZOOM Di, 22.09.2020, 10:15 - 12:00 Uhr, ZOOM Di, 22.09.2020, 08:15 - 10:00 Uhr, ZOOM Di, 29.09.2020, 10:15 - 12:00 Uhr, ZOOM Di, 06.10.2020, 10:15 - 12:00 Uhr, ZOOM Di, 06.10.2020, 08:15 - 10:00 Uhr, ZOOM Di, 13.10.2020, 10:15 - 12:00 Uhr, ZOOM Di, 20.10.2020, 10:15 - 12:00 Uhr, ZOOM Di, 20.10.2020, 08:15 - 10:00 Uhr, ZOOM Di, 27.10.2020, 10:15 - 12:00 Uhr, ZOOM Di, 03.11.2020, 10:15 - 12:00 Uhr, ZOOM Di, 03.11.2020, 08:15 - 10:00 Uhr, ZOOM Di, 10.11.2020, 10:15 - 12:00 Uhr, ZOOM Di, 17.11.2020, 10:15 - 12:00 Uhr, ZOOM Di, 17.11.2020, 08:15 - 10:00 Uhr, ZOOM Di, 24.11.2020, 10:15 - 12:00 Uhr, ZOOM Di, 01.12.2020, 10:15 - 12:00 Uhr, ZOOM Di, 01.12.2020, 08:15 - 10:00 Uhr, ZOOM Di, 15.12.2020, 10:15 - 12:00 Uhr, ZOOM Di, 15.12.2020, 08:15 - 10:00 Uhr, ZOOM Di, 05.01.2021, 08:15 - 12:00 Uhr, ZOOM (Prüfung)
Umfang	3 Semesterwochenstunden
Turnus	blocked Zoom Live Stream
Inhalt	This course provides an overview of common machine learning approaches with an emphasis on approaches that are of particular relevance for 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) Generative and autoencoder approaches. These parts will be thought both conceptually and in the form of hands-on exercises. We will mostly work with Python throughout this course. Other languages and tools (e.g., R) may be used on an as-needed basis.
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.
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. The Python course can also be attended in parallel to this course. 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 August, 31 to September 25, 2020. 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/16800973061
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 exam / 4.5 Credits
Hörer-/innen	Nach Vereinbarung
Kontakt	reto.hofstetter@unilu.ch / marc.pouly@hslu.ch