Philipp Kammerlander: Catalogue data in Autumn Semester 2023 |  |
| Name | Dr. Philipp Kammerlander |
| Address | Dep. Physik ETH Zürich, HIT G 31.2 Wolfgang-Pauli-Str. 27 8093 Zürich SWITZERLAND |
| kammerlander@itp.phys.ethz.ch | |
| Department | Physics |
| Relationship | Lecturer |
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| 363-1182-00L | New Technologies in Finance and Insurance | 3 credits | 2V | B. J. Bergmann, P. Cheridito, P. Kammerlander, J. Teichmann, R. Wattenhofer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 credit | 1V | B. J. Bergmann, P. Kammerlander | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 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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| 402-0209-00L | Quantum Physics for Non-Physicists | 6 credits | 3V + 2U | P. Kammerlander | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This is an introduction to the physics of quantum mechanics following an information-theoretical approach. We start from the basic postulates, study the behaviour of quantum systems from a single spin to entangled particles in space, and connect the learnings to groundbreaking experiments from the past and the present. This course is well-suited for students with little background in physics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course teaches the basics of quantum physics, and complements courses in quantum computation and information theory. Students are equipped with tools to tackle complex quantum mechanical problems and foundational questions. The course covers approximately the same content as QM1, but from an information-driven perspective. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Quantum formalism, from qubits to particles in space; Time and dynamics for quantum systems; Problems in 1D; Uncertainty and open systems; Spin; Problems in 3D; Non-locality and foundational aspects of quantum theory | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Quantum Processes Systems, and Information, by Benjamin Schumacher and Michael Westmoreland, available at https://www.cambridge.org/core/books/quantum-processes-systems-and-information/4E459E64E1EE7121CA2321435FAECC8A | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This course is aimed at non-physicists, and in particular at students with a background in computer science, mathematics or engineering. Basic linear algebra and calculus knowledge is required (equivalent to first-year courses). Physics knowledge is not required. Physicists and students from a different background than outlined above are welcome at their own risk. Note that while we follow an information-theoretical approach, this is not a course on quantum information theory or quantum computing. It therefore complements those courses offered at ETH Zurich. This course can be taken in parallel to Quantum Information Processing I & II. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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