Quantitative Methods for Machine Learning

Catalog description

Building upon quantitative and machine learning foundations introduced in earlier courses, CSC 851 provides an intermediate-level treatment of statistical learning, the basis of artificial intelligence and machine learning methods. The course begins with essential ideas such as bias-variance tradeoff and linear algebra as used in statistics. Additional topics include dimension reduction, model-based clustering, regularization via penalized regression, tree-based methods, and density estimation.