140.649 Essentials of Probability and Statistical Inference IV: Algorithmic and NonParametic Approaches


师资

授课教师:
Rafael Irizarry

上线日:

2006年春季课程

提供

生物统计学系(Biostatistics)


课程描述

Introduces the theory and application of modern, computationally-based methods for exploring and drawing inferences from data. Covers re-sampling methods, non-parametric regression, prediction, and dimension reduction and clustering. Specific topics include Monte Carlo simulation, bootstrap cross-validation, splines, local weighted regression, CART, random forests, neural networks, support vector machines, and hierarchical clustering. De-emphasizes proofs and replaces them with extended discussion of interpretation of results and simulation and data analysis for illustration.


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