Josef Teichmann: Katalogdaten im Herbstsemester 2023 |
| 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 | 3 KP | 2V | B. J. Bergmann, P. Cheridito, P. Kammerlander, 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 | Reinforcement Learning: Insights and Applications  Exclusively for MAS MTEC students (1st and 3rd semester). | 1 KP | 1S | C. Cuchiero, B. J. Bergmann, J. Teichmann | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Reinforcement learning (RL) is a field of machine learning that focuses on developing algorithms that enable an agent by novel machine learning technologies to learn optimal strategies through interaction with its environment. In this course we shall understand the main building blocks of (deep) RL and we shall discuss recent applications from finance and robotics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After taking this course, students will - have an understanding of the fundamentals of reinforcement learning (RL), including the definition of an agent, environment, and rewards. - understand the idea of a Markov Decision Process, which is a mathematical framework used to model decision-making problems in RL. - understand the concept of a value function, which is used to measure the expected reward an agent can receive from a given state. - review various techniques for optimizing an agent's policy to maximize its expected reward - get an idea of Deep Reinforcement Learning: We will explore the use of deep neural networks in reinforcement learning and their advantages over traditional RL methods. - will understand the concept of Partially Observed Markov Decision Processes (POMDP) and its relation to MDPs. - gain hands-on experience with RL algorithms (optional) for MDPs and POMDPs. - see applications of DRL with a discussion of the real-world applications including finance and robotics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Reinforcement learning is a subfield of machine learning that focuses on developing algorithms that enable an agent to learn through trial and error by interacting with its environment. RL differs from other ML algorithms, e.g. supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). The environment is typically stated in the form of a Markov decision process (MDP). In this course we will go through the main architecture of reinforcement learning and review some of its applications. On day 1 of the 2-day course the concept of a Markov Decision Process (MDP), its value function and the Bellmann equation are introduced and discussed. Several classical and ML powered algorithms are introduced and showcases presented. On Day 2 the concept of a partially observed Markov Decision Process is introduced. Aspects of Filtering and embedding partially observed Markov decision processes into the framework of MDPs are presented. Showcases from Robotics and Finance with an emphasis on the latter are presented in theory and applications. An understanding of basic machine learning concepts is welcomed but 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. We will provide coding examples for those you would like to follow the code. 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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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, M. Schweizer, J. Teichmann, M. V. Wüthrich | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Research colloquium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Regular research talks on various topics in mathematical finance and actuarial mathematics | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||