Bastian Jörg Bergmann: Catalogue data in Spring Semester 2025 |
| Name | Dr. Bastian Jörg Bergmann |
| Address | RiskLab ETH Zürich, HG F 42.1 Rämistrasse 101 8092 Zürich SWITZERLAND |
| bbergmann@ethz.ch | |
| Department | Management, Technology, and Economics |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||
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| 363-1153-00L | Decentralized Finance | 3 credits | 2V | B. J. Bergmann, H. Gersbach, A. Gervais | ||||||||||||||||||||||||||||||||||||||
| Abstract | DLT is emerging for a disruption of our current financial infrastructure. As such, Blockchain Finance seeks to combine open-source, peer to peer building blocks into sophisticated products using blockchain technology, seeking to disintermediate and decentralize the traditional financial service industry. This lecture will combine insights on DLT with recent applications from finance. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | At it’s core, Blockchain Finance aims to provide financial products and services on blockchain technologies. The combination of decentralized, smart-contract-based business logic solutions with a blockchain-based settlement layer facilitates the creation of financial services in a decentralized way. Traditional, functional roles of trusted third-party such as brokerage firms, banks, are replaced by smart contracts which fulfill the functions automatically. The goal if this lecture is to let you understand, - The building blocks of Distributed Ledger Technology (DLT) - Some basic applications like smart contracts, tokens, decentralized autonomous organisations (DAOs) - Limitations and concepts for overcoming centralized financial systems - Recent advances on Central Bank Digital Currencies and other applications in DeFi - The business logic behind a decentralized applications (DApps) - How a DLT project is run within a larger organization and in the start-up context The lecture will cover also guest speakers from companies, start-ups, and agencies. | |||||||||||||||||||||||||||||||||||||||||
| Content | The lecture will start with the fundamentals around blockchain technologies and smart contracts. Afterwards students learn about aspects and applications of blockchain finance, e.g. decentralised exchanges, tokenisations, digital currencies covering some theoretical and technological insights as well as insights on recent applications involving guest speakers from industry, start-ups, agencies. The focus of each session will be on the discussion part. You will be asked to prepare yourself (watch a video, read a paper, etc) for each session. Part 1: Intro to Blockchain, Focus on Exchanges, Transaction Ordering Part 2: Smart Contracts; Focus on Programming, Attacks Part 3: Decentralized Governance, DAOs and Applications Part 4: Central Bank Digital Currencies, recent advances, and approaches Part 5 & 6: DeFi applications, legal aspects, challenges, opportunities & risk in the corporate context The lecture is targeted to students across ETH with an interest in DLT. No specific coding experience is required. During the course you will follow step by step examples. For passing the course you will take online quizzes, selected exercises, and a short exam during the class. | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | There will be lecture slides to each section shared in advanced to each session. | |||||||||||||||||||||||||||||||||||||||||
| Literature | Selected readings and books are presented in each session. | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course is opened to students from all backgrounds. Some experience with quantitative disciplines such as probability and statistics, however, is useful but not mandatory. | |||||||||||||||||||||||||||||||||||||||||
Competencies |
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| 441-1000-00L | Introduction to Machine Learning in Finance and Insurance  | 4 credits | 3G | 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 |
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| 441-1001-00L | Ethics of AI | 2 credits | 1.5S | B. J. Bergmann, A. Ferrario, J. Teichmann | ||||||||||||||||||||||||||||||||||||||
| Abstract | Provides you with a comprehensive understanding of the ethical dimensions and challenges around machine learning applications in a business and societal context. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | During this course we will reflect on the integration of machine learning which raises profound ethical questions about trust, explainability, accountability, and the regulations that support the use of machine learning empowered technology in different applications. | |||||||||||||||||||||||||||||||||||||||||
| Content | Structured as an interactive workshop with guest speakers from academia and industry. | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Only open for students of the CAS ETH in Machine Learning in Finance and Insurance. | |||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 441-2000-00L | Cases in Machine Learning in Finance 1 | 2 credits | 2S | 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 |
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| 441-2001-00L | Cases in Machine Learning in Finance 2 | 2 credits | 2S | 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 |
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| 441-2002-00L | Cases in Machine Learning in Insurance | 2 credits | 2S | B. J. Bergmann, P. Cheridito, A. Ferrario | ||||||||||||||||||||||||||||||||||||||
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