This course builds foundational understanding of computational algorithms as applied in data science, with relevance to autonomous systems, finance, economics, econometrics, natural language processing, and personalized recommendations. The course covers simulation, optimization, and sequential modelling techniques for operating under uncertainty; dimensionality reduction and feature extraction for uncovering structure in high-dimensional spaces; and regularization strategies for maintaining model performance as data complexity increases.

A strong emphasis on hands-on learning runs through the course. Students work on projects including building dynamic models, implementing particle filters, exploring debiased regularization, and developing robust approaches for decision-making. By the end of the course, students have the skills to design, implement, and refine computational algorithms for machine learning and intelligent decision-making systems.