MATH 706 Modern Statistical Predictionand Data Mining
This course will train students in a variety of modern statistical and computational methods that enable researchers to learn from data and make sense of the vast amounts of data being generated in many fields. Although emphasizing applications in computer code, this course will also require mathematical proofs and derivations. Techniques taught in this course include regularization, kernel smoothing, model selection, model inference (bootstrap and EM algorithm), additive models, boosting and additive trees, neural networks, support vector machines, and ensemble learning. Particular emphasis is placed on understanding the strengths and weaknesses between different methodologies used for extracting patterns and trends from large and complex data.
Offered
Spring