Josef Teichmann: Katalogdaten im Herbstsemester 2024 |
| Name | Herr Prof. Dr. Josef Teichmann |
| Lehrgebiet | Finanzmathematik |
| Adresse | Professur für Finanzmathematik ETH Zürich, HG G 54.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telefon | +41 79 584 55 40 |
| josef.teichmann@math.ethz.ch | |
| URL | http://www.math.ethz.ch/~jteichma |
| Departement | Mathematik |
| Beziehung | Ordentlicher Professor |
| Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-1182-00L | New Technologies in Finance and Insurance Findet dieses Semester nicht statt. | 3 KP | 2V | P. Cheridito, J. Teichmann, R. Wattenhofer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Technological advances, digitization and the ability to store and process vast amounts of data has changed the landscape of financial services in recent years. This course will unpack these innovations and technologies underlying these transformations and will reflect on the impacts on the financial markets. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After taking this course, students will be able to - Understand the fundamentals of emerging technologies like supervised learning, unsupervised learning, reinforcement learning or quantum computing. - understand recent technological developments in financial services and how they drive transformation, e.g. see applications from fraud detection, credit risk assessment, portfolio optimization - reflect about the challenges of implementing machine learning in finance, e.g. data quality and availability, regulatory compliance, model interpretability and transparency, cybersecurity risks - understand the importance of continued research and development in machine learning in finance. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Overall, emerging technologies are transforming the finance and insurance industries by improving efficiency, reducing costs, enhancing customer experiences, and facilitating innovation. Hence, the financial manager of the future is commanding a wide set of skills ranging from a profound understanding of technological advances and a sensible understanding of the impact on workflows and business models. Students with an interest in finance, banking and insurance are invited to take the course without explicit theoretical knowledge in financial economics. As the course will cover topics like machine learning, cyber security, quantum computing, an understanding of these technologies is welcomed, however not mandatory. The course will also go beyond technological advances and will also cover management-related contents. Invited guest speakers will contribute to the sessions. In addition, separate networking sessions will provide entry opportunities into finance and banking. Selected guest speakers will cover different application from the field of finance and insurance, e.g. - Fraud detection: Machine learning algorithms can be trained to identify unusual patterns in financial transactions, helping to detect fraudulent activities. - Credit scoring: Machine learning can be used to develop more accurate credit scoring models, taking into account a wider range of data points than traditional models. - Investment analysis: Machine learning can be used to analyze market trends, identify potential investment opportunities, and develop predictive models for asset prices. - Risk management: Machine learning can be used to model and forecast risk, helping financial institutions to manage and mitigate risk more effectively. The course is divided in sections, each covering different areas and technologies. Students are asked to solve a short in-class exam and one out of two group exercises cases. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kompetenzen |
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| 364-1058-00L | Risk Center Seminar Series | 0 KP | 2S | H. 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, S. Wiemer, R. Zenklusen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. Students and other guests are welcome. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Participants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop novel mathematical models for open problems, to analyze them with computers, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to work scientifically on an internationally competitive level. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. For details of the program see the webpage of the colloquium. Students and other guests are welcome. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | There is no script, but a short protocol of the sessions will be sent to all participants who have participated in a particular session. Transparencies of the presentations may be put on the course webpage. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Literature will be provided by the speakers in their respective presentations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Participants should have relatively good mathematical skills and some experience of how scientific work is performed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 365-1183-00L | Cases in Machine Learning  Exclusively for MAS MTEC students (1st and 3rd semester). | 2 KP | 1S | C. Cuchiero, A. Ferrario, J. Teichmann | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Machine learning has revolutionized various domains across industry sectors. Advances in GenAI has triggered this development and has created additional fantasies for future applications. Hence, an understanding its practical applications is crucial for professionals in today’s data-driven world. This course delves into the concepts of ML, its applications and use cases and ethical considerations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After taking this course, participants will - Understand the fundamental concepts of ML with some basic hands-on cases - Understand and reflect on the ethical implications of ML algorithms, discuss bias, fairness, and transparency in AI systems. - Understand the concepts behind advances in deep learning and reinforcement learning, transfomers - Learn about applications of deep learning and reinforcement learning in finance - Learn about key areas of AI in robotics, like computer vision, imitation learning, planning, robot control - Get an overview of deep learning in different industries like logistics, automobile, healthcare - Learn about the power and limits of LLMs - Learn about prompt engineering, fine tuning and working with LLMs | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Advancements in artificial intelligence (AI) have opened up exciting opportunities across various domains. In this lecture we explore the potential and hurdles in four key areas: machine learning, deep learning, reinforcement learning, and language models across different applications, with a focus on finance and robotics. Day 1: Introduction to Machine Learning, Transparency, Interpretability and ethical aspects of ML Day 2: Introduction to Deep Learning, Reinforcement Learning, Transformers, applications in finance and robotics, overview of deep learning across industries Day 3: Focus on Large Language Models with Applications from prompt engineering and working with large language models in a business context By the end of this course, students will have a comprehensive understanding of machine learning, its ethical dimensions, and practical applications. The course is held in a workshop format with lecture and group work elements. Active participation on all course days is mandatory. Participants will work in groups on selected cases and have the opportunity to follow some basic coding examples in a Jupiter Notebook. Programming skills are not mandatory. An understanding of basic machine learning concepts is welcomed but also not mandatory (e.g. you took the class “Fundamentals on ML for Executives” or “AI for Executives”). In the beginning of the course, we will do a short primer on mathematics and statistics and some fundamental aspects of machine learning to bring all students on the same level. Grading (ungraded semester performance) is based on active participation in the class and a short written report (ungraded) after the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 401-5820-00L | Seminar in Computational Finance for CSE | 4 KP | 2S | J. Teichmann, P. Harms | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Voraussetzungen / Besonderes | Requirements: sound understanding of stochastic concepts and of con- cepts of mathematical Finance, ability to implement econometric or simula- tion routines in Python.. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 401-5910-00L | Talks in Financial and Insurance Mathematics | 0 KP | 1K | B. Acciaio, P. Cheridito, D. Possamaï, J. Teichmann | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Research colloquium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Inhalt | Regular research talks on various topics in mathematical finance and actuarial mathematics | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 441-3000-00L | Innovation Project | 3 KP | 2S | B. J. Bergmann, P. Cheridito, J. Teichmann | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This final task challenges the CAS in Machine Learning in Finance and Insurance participants to transform an inspired idea into a tangible prototype. Drawing inspiration from the workshops of Block II and Block III, you will develop and implement a pioneering project that showcases your acquired expertise. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Only open to Participants of the CAS ETH in Machine Learning in Finance and Insurance | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Details on Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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