Quantitative finance is applied in the industry, mostly through software implementations of computational algorithms. Often, the mathematical methods employed involve more or less sophisticated methods; the accuracy, speed, and resource consumption of such algorithms often mark the difference between a business workflow which is productive, useful, and economic, and others which are slow, inefficient, resource intensive, or simply inaccurate and therefore potentially useless.

With the introduction of data science, machine learning, and other methods from artificial intelligence into the world of mathematical finance, computational challenges are increasing due to the larger computational ambition of new algorithms and the expansion of traditional business lines.

Aligning innovation and discovery of efficient numerical methods with the development of new business lines, this course will develop best practices in the design of numerical methods for the efficient design of computational algorithms across a wide scope of mathematical implementations, ranging from the traditional areas of computational algebra and differential equations to the newer ones of computational graph theory and optimization.