Essential Numerical Methods for Data Analytics and Financial Engineering

This course introduces essential numerical methods to students who are interested in data analytics and financial engineering. Major topics of the course include Introduction to supervised/unsupervised learning, including Kernel methods, support vector machines, K-means clustering, neural networks; Numerical methods for financial engineering with a focus on Monte Carlo methods importance sampling, and various variance reductions techniques. Applications of these methods to practical problems arising from data analytics and financial engineering (e.g. option pricing) will be discussed. These methods also find many applications in various scientific fields, so students interested in scientific computing will benefit from this course as well.