Patrick Cheridito: Catalogue data in Spring Semester 2025

Name	Prof. Dr. Patrick Cheridito
Field	Insurance Mathematics
Address	Dep. Mathematik ETH Zürich, HG F 42.3 Rämistrasse 101 8092 Zürich SWITZERLAND
Telephone	+41 44 633 87 87
E-mail	patrick.cheridito@math.ethz.ch
URL	http://www.math.ethz.ch/~patrickc
Department	Mathematics
Relationship	Full Professor

Number

Title

ECTS

Hours

Lecturers

364-1058-00L

Risk Center Seminar Series

Does not take place this semester.

0 credits

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

Abstract

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.

Learning objective

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.

Content

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.

Lecture notes

The sessions are recorded whenever possible and posted on the ETH Risk Center webpage. If available, presentation slides are shared as well.

Literature

Each speaker will provide a literature review.

Prerequisites / Notice

In most cases, a quantitative background is required. Depending on the topic, field-specific knowledge may be required.

Competencies

Subject-specific Competencies	Concepts and Theories	fostered
	Techniques and Technologies	fostered
Method-specific Competencies	Analytical Competencies	fostered
	Decision-making	fostered
	Media and Digital Technologies	fostered
	Problem-solving	fostered
	Project Management	fostered
Social Competencies	Communication	fostered
	Cooperation and Teamwork	fostered
	Customer Orientation	fostered
	Leadership and Responsibility	fostered
	Self-presentation and Social Influence	fostered
	Sensitivity to Diversity	fostered
	Negotiation	fostered
Personal Competencies	Adaptability and Flexibility	fostered
	Creative Thinking	fostered
	Critical Thinking	fostered
	Integrity and Work Ethics	fostered
	Self-awareness and Self-reflection	fostered
	Self-direction and Self-management	fostered

401-3916-25L

Machine Learning for Finance and Complex Systems

Maximal number of participants: 42

5 credits

N. Antulov-Fantulin, P. Cheridito

Abstract

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.

Learning objective

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).

Content

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.

Literature

[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.

Prerequisites / Notice

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.

Competencies

Subject-specific Competencies	Concepts and Theories	assessed
	Techniques and Technologies	assessed
Method-specific Competencies	Analytical Competencies	assessed
	Decision-making	assessed
	Problem-solving	assessed
	Project Management	assessed
Social Competencies	Communication	assessed
	Cooperation and Teamwork	assessed
	Self-presentation and Social Influence	assessed
	Sensitivity to Diversity	assessed
Personal Competencies	Adaptability and Flexibility	assessed
	Creative Thinking	assessed
	Critical Thinking	assessed
	Integrity and Work Ethics	assessed
	Self-awareness and Self-reflection	assessed
	Self-direction and Self-management	assessed

401-5910-00L

Talks in Financial and Insurance Mathematics

0 credits

B. Acciaio, P. Cheridito, D. Possamaï, M. Schweizer, J. Teichmann, M. V. Wüthrich

Abstract

Research colloquium

Learning objective

Introduction to current research topics in "Insurance Mathematics and Stochastic Finance".

Content

https://www.math.ethz.ch/imsf/courses/talks-in-imsf.html

441-1000-00L

Introduction to Machine Learning in Finance and Insurance

4 credits

B. J. Bergmann, P. Cheridito, A. Ferrario, J. Teichmann

Abstract

Provides you with a comprehensive introduction to the fundamentals of machine learning, including key concepts, algorithms, and practical applications.

Learning objective

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

Content

Introduction to Machine Learning with cases.

Prerequisites / Notice

Only for students of the CAS ETH in Machine Learning in Finance and Insurance

Competencies

Subject-specific Competencies	Concepts and Theories	assessed
	Techniques and Technologies	assessed
Method-specific Competencies	Analytical Competencies	fostered
	Problem-solving	fostered
Social Competencies	Cooperation and Teamwork	fostered
Personal Competencies	Creative Thinking	fostered
	Critical Thinking	fostered

441-2000-00L

Cases in Machine Learning in Finance 1

2 credits

B. J. Bergmann, P. Cheridito, J. Teichmann

Abstract

This course provides you with real-world case studies and projects in finance and insurance where machine learning methods have been successfully applied.

Learning objective

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

Content

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.

Prerequisites / Notice

This course is only open to students from the CAS ETH in Machine Learning in Finance and Insurance.

Competencies

Subject-specific Competencies	Concepts and Theories	fostered
	Techniques and Technologies	fostered
Method-specific Competencies	Analytical Competencies	fostered
	Problem-solving	fostered
	Project Management	fostered
Social Competencies	Communication	fostered
	Leadership and Responsibility	fostered
Personal Competencies	Adaptability and Flexibility	fostered
	Creative Thinking	fostered
	Critical Thinking	fostered
	Integrity and Work Ethics	fostered

441-2001-00L

Cases in Machine Learning in Finance 2

2 credits

B. J. Bergmann, P. Cheridito, J. Teichmann

Abstract

This course provides you with real-world case studies and projects in finance and insurance where machine learning methods have been successfully applied.

Learning objective

Content

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.

Prerequisites / Notice

This course is only open to students from the CAS ETH in Machine Learning in Finance and Insurance.

Competencies

Subject-specific Competencies	Concepts and Theories	fostered
	Techniques and Technologies	fostered
Method-specific Competencies	Analytical Competencies	fostered
	Problem-solving	fostered
	Project Management	fostered
Social Competencies	Communication	fostered
	Cooperation and Teamwork	fostered
	Leadership and Responsibility	fostered
Personal Competencies	Adaptability and Flexibility	fostered
	Creative Thinking	fostered
	Critical Thinking	fostered
	Integrity and Work Ethics	fostered

441-2002-00L

Cases in Machine Learning in Insurance

2 credits

B. J. Bergmann, P. Cheridito, A. Ferrario

Abstract

Learning objective

441-2003-00L

Cases in Machine Learning: Fintech & Startups

Does not take place this semester.

2 credits

P. Cheridito, J. Teichmann

Abstract

This course provides you with real-world case studies and projects in finance and insurance where machine learning methods have been successfully applied.

Learning objective

Content

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.

Prerequisites / Notice

This course is only open to students from the CAS ETH in Machine Learning in Finance and Insurance.

Competencies

Subject-specific Competencies	Concepts and Theories	fostered
	Techniques and Technologies	fostered
Method-specific Competencies	Analytical Competencies	fostered
	Problem-solving	fostered
	Project Management	fostered
Social Competencies	Communication	fostered
	Leadership and Responsibility	fostered
Personal Competencies	Adaptability and Flexibility	fostered
	Creative Thinking	fostered
	Critical Thinking	fostered
	Integrity and Work Ethics	fostered