We will model and solve scientific problems with partial differential equations. Differential equations which are important in applications will be classified and solved. Elliptic, parabolic and hyperbolic differential equations will be treated. The following mathematical tools will be introduced: Laplace and Fourier transforms, Fourier series, separation of variables, methods of characteristics.

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.

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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.

This course introduces methods from probability theory and statistics that can be used to model financial risks. Topics addressed include loss distributions, risk measures, extreme value theory, multivariate models, copulas, dependence structures, backtesting, and operational risk.

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 introduces machine learning methods that can be used for modelling and analysing complex systems with a particular focus on financial applications.

This course introduces machine learning methods that can be used in finance and insurance applications.

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

This course introduces methods from probability theory and statistics that can be used to model financial risks. Topics addressed include loss distributions, risk measures, extreme value theory, multivariate models, copulas, dependence structures, backtesting, and operational risk.

The following concepts are covered: probabilities, random variables, probability distributions, joint and conditional probabilities and distributions, law of large numbers, central limit theorem, descriptive statistics, statistical inference, parameter estimation, confidence intervals, statistical tests, two-sample tests, linear regression.

This is an introduction to probability, statistics, and machine learning for students of mechanical engineering. We cover the fundamental concepts from probability theory, statistics and machine learning, with a focus on applications for mechanical engineering.