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The Fields Institute

This bootcamp will focus on advanced machine learning techniques currently being used in the financial industry, as well as novel techniques at the forefront of academic research. Topics will include unsupervised and supervised machine learning, data mining and applications in predictive and risk analytics, fraud detection and consumer finance, deep reinforcement learning, natural language processing, high-frequency models of financial markets, and credit risk analytics.

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

This workshop aims to bring together a diverse, multi-disciplinary cross-section of pioneering researchers to foster a community, share ideas, and work together to build a lasting mathematical and social foundation. The key themes of this workshop include i) neural network design and approximability; ii) modeling, inference, prediction, and data assimilation; and iii) high-performance algorithms and scalability. The participants of this workshop possess complementary expertise in numerical analysis, approximation theory, functional analysis, probability theory, nonlinear programming, high-performance computing, computer science, statistics, engineering, and industrial applications. Putting all this knowledge together will create a synergy of effects that will profoundly impact the rigorous development of new and lasting machine learning methods for scientific computing applications.

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

In this workshop, we will explore how the research realms of generative modelling and 3D neural representations can benefit from each other, as well as explore novel applications in different areas of science and technology.

Simulation,    Wissenschaftliches Rechnen und Datenwissenschaft

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

This workshop focuses on addressing general theoretical problems that underpin cutting-edge applications of linear algebra, including low-rank approximation and tensor decomposition, optimization, machine learning, nonlocal network dynamics, and uncertainty quantification. By bringing together experts from these different communities at a BIRS workshop, new insights in applied linear algebra will be brought to these challenging problems, which we expect to generate significant advances to state-of-the-art computational algorithms in the next decade.

Algebra,    Komplexe Netzwerke