363-1100-00L  Risk Case Study Challenge

SemesterAutumn Semester 2020
LecturersA. Bommier, S. Feuerriegel, J. Teichmann
Periodicityyearly recurring course
CourseDoes not take place this semester.
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
363-1100-00 SRisk Case Study Challenge Special students and auditors need a special permission from the lecturers.
Does not take place this semester.
The course takes place in FS 2021.
2 hrsA. Bommier, S. Feuerriegel, J. Teichmann

Catalogue data

AbstractThis 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.
ObjectiveStudents 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.
ContentGet 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 / NoticePlease apply for this course via the official website (Link). Apply no later than September 13, 2019.
The number of participants is limited to 16.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersB. J. Bergmann, A. Bommier, S. Feuerriegel, J. Teichmann
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

General : Special students and auditors need a special permission from the lecturers
PlacesLimited number of places. Special selection procedure.
Beginning of registration periodRegistration possible from 30.08.2020
Waiting listuntil 20.09.2020
End of registration periodRegistration only possible until 13.09.2020

Offered in

ProgrammeSectionType
Data Science MasterSeminarWInformation
Management, Technology and Economics MasterElective CoursesWInformation
MAS in Management, Technology, and EconomicsElectives, 1. and 3. SemesterWInformation
Quantitative Finance MasterMathematical Methods for FinanceWInformation
Statistics MasterSeminar or Semester PaperWInformation