Bastian Jörg Bergmann: Katalogdaten im Herbstsemester 2023 |
| Name | Herr Dr. Bastian Jörg Bergmann |
| Adresse | Risk Center ETH Zürich, SEC D 3 Scheuchzerstrasse 7 8092 Zürich SWITZERLAND |
| Telefon | +41 44 632 63 34 |
| bbergmann@ethz.ch | |
| Departement | Management, Technologie und Ökonomie |
| Beziehung | Dozent |
| 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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| 365-1181-00L | Introduction to Quantum Computing: Current Challenges and Business Insights  Exclusively for MAS MTEC students (1st and 3rd semester). | 1 KP | 1V | B. J. Bergmann, P. Kammerlander | ||||||||||||||||||||||||||||||||
| Kurzbeschreibung | In recent years quantum computing has become one of the most talked-about technological promises yet it is still often misunderstood. This 2-day course will give you an introduction to the basic principles of quantum computing and related technologies with lectures from both academic experts and business leaders. | |||||||||||||||||||||||||||||||||||
| Lernziel | After taking this course, students will - have a basic, pragmatic, and practical understanding of quantum computing: how it works, what makes it different from classical computing, what kinds of problems it may be useful for, and what kinds of problems it won’t be useful for - be able to judge the real-world impact of quantum computing today and in the coming years, as well as the challenges and opportunities it poses with respect to data security, simulation of complex systems, optimization problems, and AI/ML, to name a few examples - be able to name and explain on a high level other quantum technologies (besides quantum computing) that may have a significant impact on the market, now or in the future - be able to explain examples of business models in the area of quantum technology - have had hands-on experience from working at challenges in developing business models in the quantum technology sector - have had the chance to network and facilitate contacts with companies and experts at local research institutions and players in the local quantum technologies network | |||||||||||||||||||||||||||||||||||
| Inhalt | Quantum computing is a type of computing that uses quantum mechanics principles, such as superposition and entanglement, to process information. Unlike classical computers, which store information in bits (either 0 or 1), quantum computers use quantum bits, or qubits, which can exist in multiple states simultaneously. However, quantum computing is still in its early stages of development and faces significant challenges, such as maintaining the stability of qubits and minimizing errors due to environmental noise. On day 1 of the 2-day course there will be introductory lectures to quantum computing and related quantum technologies such as quantum communication, quantum sensing, and quantum simulation by experts from academia. You will get an overview of Quantum mechanics, quantum computing algorithms as well as quantum hardware. In addition, we will offer lab tours where state-of-the-art quantum computing equipment can be seen in action, presented by scientists doing cutting-edge research at ETH Zurich. Guest lectures from Swiss businesses in the field of quantum technologies will share their view on the current and future market and present their companies’ histories, strategies, and goals. Together we will discuss some of the current challenges facing quantum computing as well as potential future directions for research and development in this field. On day 2, further guest lectures will present challenges on which the students can work in teams, followed by a final round of presentations and feedback. The students will benefit from first-hand insights by experts in the field with diverse backgrounds (academic, startup, business, industrial). Grading (ungraded semester performance) is based on active participation on both days. | |||||||||||||||||||||||||||||||||||
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| 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. | |||||||||||||||||||||||||||||||||||
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