Stefan Feuerriegel: Catalogue data in Autumn Semester 2020
|Name|| Prof. Dr. Stefan Feuerriegel|
(Professor Ludwig-Maximilians Universität München)
|Field||Management Information Systems|
|Department||Management, Technology, and Economics|
|Relationship||Assistant Professor (Tenure Track)|
Entry level course in management for BSc, MSc and PHD students at all levels not belonging to D-MTEC. This course can be complemented with Discovering Management (Excercises) 351-0778-01.
|3 credits||3G||B. Clarysse, S. Brusoni, S. Feuerriegel, G. Grote, V. Hoffmann, T. Netland, G. von Krogh|
|Abstract||Discovering Management offers an introduction to the field of business management and entrepreneurship for engineers and natural scientists. The module provides an overview of the principles of management, teaches knowledge about management that is highly complementary to the students' technical knowledge, and provides a basis for advancing the knowledge of the various subjects offered at D-MTEC.|
|Objective||Discovering Management combines in an innovate format a set of theory lectures and a series of case studies. The learning model for Discovering Management involves 'learning by doing'. The objective is to introduce the students to the relevant topics of the management literature and give them a good introduction in entrepreneurship topics too. The course is a series of lectures on the topics of strategy, innovation, leadership, productions and operations management and corporate social responsibility. While the different theory lectures provide the theoretical and conceptual foundations, the experiential learning outcomes result from the case studies.|
|Content||Discovering Management aims to broaden the students' understanding of the principles of business management, emphasizing the interdependence of various topics in the development and management of a firm. The lectures introduce students not only to topics relevant for managing large corporations, but also touch upon the different aspects of starting up your own venture. The lectures will be presented by the respective area specialists at D-MTEC.|
The course broadens the view and understanding of technology by linking it with its commercial applications and with society. The lectures are designed to introduce students to topics related to strategy, corporate innovation, leadership, value chain analysis, corporate social responsibility, and information management. Practical examples from case studies will stimulate the students to critically assess these issues.
|Prerequisites / Notice||Discovering Management is designed to suit the needs and expectations of Bachelor students at all levels as well as Master and PhD students not belonging to D-MTEC. By providing an overview of Business Management, this course is an ideal enrichment of the standard curriculum at ETH Zurich.|
No prior knowledge of business or economics is required to successfully complete this course.
|363-1100-00L||Risk Case Study Challenge |
Does not take place this semester.
|3 credits||2S||A. Bommier, S. Feuerriegel, J. Teichmann|
|Abstract||This seminar provides master students at ETH with the challenging opportunity of working on a real risk case in close collaboration with a company. For Fall 2019 the Partner will be Credit Suisse and the topic of cases will focus on machine learning applications in finance.|
|Objective||Students work in groups on a real risk-related case of a business relevant topic provided by experts from Risk Center partners. While gaining substantial insights into the risk modeling and management of the industry, students explore the case or problem on their own, working in teams, and develop possible solutions. The cases allow students to use logical problem solving skills with emphasis on evidence and application and involve the integration of scientific knowledge. Typically, the cases can be complex, cover ambiguities, and may be addressed in more than one way. During the seminar, students visit the partners’ headquarters, interact and conduct interviews with risk professionals. The final results will be presented at the partners' headquarters.|
|Content||Get a basic understanding of |
o Risk management and risk modelling
o Machine learning tools and applications
o How to communicate your results to risk professionals
For that you work in a group of 4 students together with a Case Manager from the company.
In addition you are coached by the Lecturers on specific aspects of machine learning as well as communication and presentation skills.
|Prerequisites / Notice||Please apply for this course via the official website (www.riskcenter.ethz.ch/education/lectures/risk-case-study-challenge-.html). Apply no later than September 13, 2019.|
The number of participants is limited to 16.
|364-1064-00L||Inaugural Seminar - Doctoral Retreat |
Pre-registration upon invitation required.
Once your pre-registration has been confirmed, a registration in myStudies is possible.
|1 credit||1S||S. Feuerriegel, S. Brusoni, R. Finger, T. Netland, F. von Wangenheim|
|Abstract||This course is geared towards first and second-year doctoral candidates of MTEC. It is held as in a workshop style. Students attending this seminar will benefit from interdisciplinary discussions and insights into current and future work in business and economics research.|
|Objective||The purpose of this course is to|
- introduce doctoral candidates to the world of economics, management and systems research at MTEC
- make doctoral candidates aware of silo-thinking in the specific sub-disciplines and encourage them to go beyond those silos
- discuss current issues with regard to substantive, methodological and theoretical domains of research in the respective fields
|364-1105-00L||Bayesian Data Science|
Does not take place this semester.
Exclusively for PhD studies.
|1 credit||S. Feuerriegel|
|Abstract||This course introduces to the Bayesian approach to statistical modeling and further covers on how to formulate and evaluate Bayesian models.|
|Objective||Students will gain the ability to|
- understand the difference between frequentist statistics and Bayesian approaches
- formalize and implement Bayesian models in R/Stan.
- evaluate estimated models.
|Literature||Students are asked to prepare Chapters 2 and 3 of the following book prior to the first course data:|
Richard McElreath (2016). Statstical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press.