Quantitative Foundations

Catalog description

This course provides an introduction in the fundamentals of working with quantitative information. Topics include matrix algebra (matrices, vectors, direct and indirect methods for solving linear systems, eigenvectors, singular value decomposition, least-squares systems) optimization (convex analysis, gradient descent, Newton's method, interior-point methods), statistics (random variables, p-values, hypothesis testing, confidence intervals) and data exploration (clustering, dimensionality reduction, curve fitting). Note: Knowledge in probability and statistics calculus, and computer programming or permission of instructor is required.