Universität Wien
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040171 SE Artificial Intelligence and the Multinational Company (MA) (2024W)

International Business

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 24 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Donnerstag 17.10. 13:15 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
  • Freitag 25.10. 13:15 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
  • Freitag 08.11. 13:15 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
  • Donnerstag 21.11. 09:00 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
  • Montag 25.11. 13:15 - 16:30 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 09.12. 13:15 - 16:30 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 17.12. 11:30 - 12:00 Digital

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Contents
In this class, students will learn to understand artificial intelligence (AI) and the role it plays for multinational companies (MNCs). We will discuss AI as a tool for business analytics in context-dependent MNCs, we will introduce large language models and how they can be used for business, and we will discuss how MNCs can change to integrate AI and face the challenges resulting from AI integration, with a particular focus on the global dispersion of business activities. We will solve simple isolated exercises, as well as more involved issues in business case studies in an international context. After completing this course, students will be able to run machine learning algorithms, to understand the basics and basic applications of language models, and to understand how these tasks contribute to corporate strategy and competitive advantage in a complex international context. Students taking this class are expected to have a basic understanding of statistics. Although helpful, no prior experience with programming languages is required.

Methods
The class is a workshop-style course, with many interactive elements. Students are expected to give presentations, provide feedback on each other’s work, and discuss their progress with instructors.

Learning outcomes
Students gain insight into machine learning, language models, and their applications in MNCs subject to different institutional contexts.

https://international-business.univie.ac.at/studies/master-courses/

Art der Leistungskontrolle und erlaubte Hilfsmittel

Case study report: 30%
Case study presentation: 20%
Homework exercises: 20%
Class participation 15%
Test in session one: 10%
Peer feedback: 5%

Mindestanforderungen und Beurteilungsmaßstab

Indication of references and tools
Students must indicate any sources, tools, or services they used to create (parts of) submissions (e.g., homework, exams, presentations). This extends, among other things, to all sorts of literature references (e.g., journal articles, books), as well as packages of statistical software (e.g., R, Python) and text-generation programs (e.g., ChatGPT). Failure to do so is considered as violation of good academic practice.

Prüfungsstoff

Literatur

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 08.01.2025 12:05