MATH 437 - Modern Approaches to Data Analysis (4)

Nonparametric statistical inference, including methods based on rank and order; resampling, including bootstrap; smoothing histograms, including kernel and smoothing-spines; clustering with logistic and multinomial models, hierarchical clustering and k-means; inference based on posterior distributions, learning and neural networks. (3 hours lecture, 2 hours activity)

Prerequisite: MATH 335 with a C (2.0) or better.

Undergraduate Course not available for Graduate Credit