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.

This course provides you with real-​world case studies and projects in finance and insurance where machine learning methods have been successfully applied.

This course provides you with real-​world case studies and projects in finance and insurance where machine learning methods have been successfully applied.

This course provides you with real-​world case studies and projects in finance and insurance where machine learning methods have been successfully applied.

This course is on the numerical approximations of stochastic ordinary differential equations (SDEs) driven by Brownian motions and Lévy processes. SDEs have several applications, for example in financial engineering.The contents cover stochastic processes, stochastic calculus, well-posedness results for SDEs, strong and weak approximations of SDEs, and simulation via Monte Carlo methods.

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.

Provides you with a comprehensive understanding of the ethical dimensions and challenges around machine learning applications in a business and societal context.

Banach and Hilbert spaces, bounded linear operators; Hahn Banach, Baire Category, Uniform boundedness and Banach Steinhaus Theorem, open mapping/closed graph theorem; convexity; dual spaces; weak and weak* topologies; Banach-Alaoglu; reflexive spaces; Uniformly Convex Spaces; Application to L^p Spaces; Compact operators, Spectral theory of self-adjoint compact operators. Sobolev spaces.

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.

Provides you with a comprehensive introduction to the fundamentals of machine learning, including key concepts, algorithms, and practical applications.

This course is a practical, hands-on introduction to various aspects of modelling, dealing with and managing risks across different industries, contexts and applications.

Rigorous proofs & many coding excursions for the following topics: Universal approximation theorems, Stochastic gradient Descent, Deep networks and wavelet analysis, Deep Hedging, Deep calibration, Different network architectures, Reservoir Computing, Time series analysis by machine learning, Reinforcement learning, generative adversarial networks, Economic games, Large Language Models in Finance.

The course will deal with the following topics with rigorous proofs and many coding excursions: Universal approximation theorems, Stochastic gradient Descent, Deepnetworks and wavelet analysis, Deep Hedging, Deep calibration,Different network architectures, Reservoir Computing, Time series analysis by machine learning, Reinforcement learning, generative adversersial networks, Economic games.

Advanced course on mathematical finance:- semimartingales and general stochastic integration- absence of arbitrage and martingale measures- fundamental theorem of asset pricing- option pricing and hedging- hedging duality- optimal investment problems- additional topics

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.

Introduction to probability and statistics

Probability and Statistics

Introduction to probability theory and statistics

- Diskrete Wahrscheinlichkeitsräume- Stetige Modelle- Grenzwertsätze- Einführung in die Statistik

This Risk Case Study Challenge gives MSc students the challenging opportunity to work on a real risk-modelling and/or risk-management case in close collaboration with a Risk Center corporate partner. The Corporate Partner for the Spring 2022 Edition will be announced soon.