Patrick Cheridito: Katalogdaten im Frühjahrssemester 2025 |
| Name | Herr Prof. Dr. Patrick Cheridito |
| Lehrgebiet | Versicherungsmathematik |
| Adresse | Dep. Mathematik ETH Zürich, HG F 42.3 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telefon | +41 44 633 87 87 |
| patrick.cheridito@math.ethz.ch | |
| URL | http://www.math.ethz.ch/~patrickc |
| Departement | Mathematik |
| Beziehung | Ordentlicher Professor |
| Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-1058-00L | Risk Center Seminar Series Findet dieses Semester nicht statt. | 0 KP | 2S | 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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | In 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Participants 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Academic presentations from ETH faculty as well as external researchers. Each seminar is followed by a Q&A session and (when permitted) a networking Apéro. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | The sessions are recorded whenever possible and posted on the ETH Risk Center webpage. If available, presentation slides are shared as well. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Each speaker will provide a literature review. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | In most cases, a quantitative background is required. Depending on the topic, field-specific knowledge may be required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kompetenzen |
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| 401-3916-25L | Machine Learning for Finance and Complex Systems  Maximal number of participants: 42 | 5 KP | 3G | N. Antulov-Fantulin, P. Cheridito | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course introduces machine learning methods and frameworks that can be used for modelling and analysing complex systems with a particular focus on financial time series. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course has two main objectives: (i) theoretical - to provide an overview of machine learning methods with a focus on complex systems and financial time series; (ii) practical - to allow students to gain practical experience by working on a coding project based on a theoretical topic of part (i). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Introduction to complex systems, empirical facts in finance, introduction to PyTorch, ensemble learning, neural networks, clustering, GraphCuts, matrix factorisation, reinforcement learning, MCMC, LSTM, attention mechanism, neural ODEs, PINNs, transformers, Black–Litterman model. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | [1] Paszke, Adam, et al. "Pytorch: An imperative style, high-performance deep learning library." Advances in neural information processing systems 32 (2019). [2] Ian Goodfellow, Yoshua Bengio and Aaron Courville (2020). Deep Learning. MIT Press. [3] Mehta, Pankaj, et al. "A high-bias, low-variance introduction to machine learning for physicists." Physics reports 810 (2019): 1-124. [4] Tsay, Ruey S. Analysis of financial time series. John wiley & sons, 2005. [5] Richmond, Peter, Jürgen Mimkes, and Stefan Hutzler. Econophysics and physical economics. Oxford University Press, USA, 2013. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites: Machine Learning in Finance and Insurance. Max 40 students, due to guided projects. Topics are defined at the beginning of the course. They consist of different research papers that have to be analyzed, reproduced and potentially extended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 401-5910-00L | Talks in Financial and Insurance Mathematics | 0 KP | 1K | B. Acciaio, P. Cheridito, D. Possamaï, M. Schweizer, J. Teichmann, M. V. Wüthrich | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Forschungskolloquium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Einfuehrung in aktuelle Forschungsthemen aus dem Bereich "Insurance Mathematics and Stochastic Finance". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | https://www.math.ethz.ch/imsf/courses/talks-in-imsf.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 441-1000-00L | Introduction to Machine Learning in Finance and Insurance | 4 KP | 3G | B. J. Bergmann, P. Cheridito, A. Ferrario, J. Teichmann | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Provides you with a comprehensive introduction to the fundamentals of machine learning, including key concepts, algorithms, and practical applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | You will gain a solid foundation in machine learning and develop the skills to build and evaluate machine learning models for various tasks in the following blocks and modules for the CAS ETH in Machine Learning in Finance and Insurance | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Introduction to Machine Learning with cases. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Only for students of the CAS ETH in Machine Learning in Finance and Insurance | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 441-2000-00L | Cases in Machine Learning in Finance 1 | 2 KP | 2S | B. J. Bergmann, P. Cheridito, J. Teichmann | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides you with real-world case studies and projects in finance and insurance where machine learning methods have been successfully applied. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Get exposure to real-world case studies and projects in finance and insurance where ML methods have been successfully applied. Gain insights and understanding of the overall system landscape & architecture in which your machine learning model is embedded. Choose and deep dive into cases and applications guided by ETH faculty and professionals from finance, banking and insurance | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Structured as an interactive workshop. Students select 3 out of 4 workshops offered between June, July and September. Workshops take place at ETH or at corporate facilities. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This course is only open to students from the CAS ETH in Machine Learning in Finance and Insurance. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 441-2001-00L | Cases in Machine Learning in Finance 2 | 2 KP | 2S | B. J. Bergmann, P. Cheridito, J. Teichmann | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides you with real-world case studies and projects in finance and insurance where machine learning methods have been successfully applied. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Get exposure to real-world case studies and projects in finance and insurance where ML methods have been successfully applied. Gain insights and understanding of the overall system landscape & architecture in which your machine learning model is embedded. Choose and deep dive into cases and applications guided by ETH faculty and professionals from finance, banking and insurance | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Structured as an interactive workshop. Students select 3 out of 4 workshops offered between June, July and September. Workshops take place at ETH or at corporate facilities. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This course is only open to students from the CAS ETH in Machine Learning in Finance and Insurance. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 441-2002-00L | Cases in Machine Learning in Insurance | 2 KP | 2S | B. J. Bergmann, P. Cheridito, A. Ferrario | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 441-2003-00L | Cases in Machine Learning: Fintech & Startups Findet dieses Semester nicht statt. | 2 KP | 2S | P. Cheridito, J. Teichmann | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides you with real-world case studies and projects in finance and insurance where machine learning methods have been successfully applied. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Get exposure to real-world case studies and projects in finance and insurance where ML methods have been successfully applied. Gain insights and understanding of the overall system landscape & architecture in which your machine learning model is embedded. Choose and deep dive into cases and applications guided by ETH faculty and professionals from finance, banking and insurance. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Structured as an interactive workshop. Students select 3 out of 4 workshops offered between June, July and September. Workshops take place at ETH or at corporate facilities. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This course is only open to students from the CAS ETH in Machine Learning in Finance and Insurance. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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