Josef Teichmann: Catalogue data in Spring Semester 2023

Name Prof. Dr. Josef Teichmann
FieldFinancial Mathematics
Address
Professur für Finanzmathematik
ETH Zürich, HG G 54.2
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 79 584 55 40
E-mailjosef.teichmann@math.ethz.ch
URLhttp://www.math.ethz.ch/~jteichma
DepartmentMathematics
RelationshipFull Professor

NumberTitleECTSHoursLecturers
364-1058-00LRisk Center Seminar Series0 credits2SH. Schernberg, D. Basin, A. Bommier, D. N. Bresch, S. Brusoni, L.‑E. Cederman, P. Cheridito, F. Corman, H. Gersbach, C. Hölscher, K. Paterson, G. Sansavini, B. Stojadinovic, B. Sudret, J. Teichmann, R. Wattenhofer, U. A. Weidmann, S. Wiemer, R. Zenklusen
AbstractIn this series of seminars, invited speakers discuss various topics in the area of risk modelling, governance of complex socio-economic systems, managing risks and crises, and building resilience. Students, PhD students, post-docs, faculty and individuals outside ETH are welcome.
Learning objectiveParticipants gain insights in a broad range of risk- and resilience-related topics. They expand their knowledge of the field and deepen their understanding of the complexity of our social, economic and engineered systems. For young researchers in particular, the seminars offer an opportunity to learn academic presentation skills and to network with an interdisciplinary scientific audience.
ContentAcademic presentations from ETH faculty as well as external researchers.
Each seminar is followed by a Q&A session and (when permitted) a networking Apéro.
Lecture notesThe sessions are recorded whenever possible and posted on the ETH Risk Center webpage. If available, presentation slides are shared as well.
LiteratureEach speaker will provide a literature review.
Prerequisites / NoticeIn most cases, a quantitative background is required. Depending on the topic, field-specific knowledge may be required.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesfostered
Techniques and Technologiesfostered
Method-specific CompetenciesAnalytical Competenciesfostered
Decision-makingfostered
Media and Digital Technologiesfostered
Problem-solvingfostered
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingfostered
Critical Thinkingfostered
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
401-0614-00LProbability and Statistics Information 5 credits2V + 2UJ. Teichmann
AbstractIntroduction to probability theory and statistics
Learning objectivea) Fähigkeit, die behandelten wahrscheinlichkeitstheoretischen Methoden zu verstehen und anzuwenden

b) Probabilistisches Denken und stochastische Modellierung

c) Fähigkeit, einfache statistische Tests selbst durchzuführen und die Resultate zu interpretieren
ContentWahrscheinlichkeitsraum, Wahrscheinlichkeitsmass, Zufallsvariablen, Verteilungen, Dichten, Unabhängigkeit, bedingte Wahrscheinlichkeiten, Erwartungswert, Varianz, Kovarianz, Gesetz der grossen Zahlen, Zentraler Grenzwertsatz, grosse Abweichungen, Chernoff-Schranken, Maximum-Likelihood-Schätzer, Momentenschätzer, Tests, Neyman-Pearson Lemma, Konfidenzintervalle
401-3932-DRLMathematics for New Technologies in Finance Information Restricted registration - show details
Only for ETH D-MATH doctoral students and for doctoral students from the Institute of Mathematics at UZH. The latter need to send an email to Jessica Bolsinger (info@zgsm.ch) with the course number. The email should have the subject „Graduate course registration (ETH)“.

Formerly until FS22: Machine Learning in Finance
2 credits3V + 1UJ. Teichmann
AbstractThe course will deal with the following topics with rigorous proofs and many coding excursions: Universal approximation theorems, Stochastic gradient Descent, Deep
networks and wavelet analysis, Deep Hedging, Deep calibration,
Different network architectures, Reservoir Computing, Time series analysis by machine learning, Reinforcement learning, generative adversersial networks, Economic games.
Learning objective
401-3932-19LMathematics for New Technologies in Finance Information
formerly until FS22: Machine Learning in Finance
4 credits3V + 1UJ. Teichmann
AbstractThe course will deal with the following topics with rigorous proofs and many coding excursions: Universal approximation theorems, Stochastic gradient Descent, Deep
networks and wavelet analysis, Deep Hedging, Deep calibration,
Different network architectures, Reservoir Computing, Time series analysis by machine learning, Reinforcement learning, generative adversersial networks, Economic games.
Learning objective
ContentCATALOGUE DATA TO BE ADJUSTED
Prerequisites / NoticeBachelor in mathematics, physics, economics or computer science.
401-5820-00LSeminar in Computational Finance for CSE4 credits2SJ. Teichmann
Abstract
Learning objective
401-5910-00LTalks in Financial and Insurance Mathematics Information 0 credits1KB. Acciaio, P. Cheridito, D. Possamaï, M. Schweizer, J. Teichmann, M. V. Wüthrich
AbstractResearch colloquium
Learning objectiveIntroduction to current research topics in "Insurance Mathematics and Stochastic Finance".
Contenthttps://www.math.ethz.ch/imsf/courses/talks-in-imsf.html