Essentials of Probability and Statistical Inference IV: Algorithmic and Nonparametric Approaches

Author:

Irizarry, Rafael

Subject:

Mathematics and Statistics, Science and Technology, Social Sciences

Institution Name:

Johns Hopkins Bloomberg School of Public Health

Collection:

JHSPH OpenCourseWare

Grade Level:

Post-secondary

Abstract:

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.

Languages:

English

Material Type:

Full Course, Lecture Notes, Syllabi

Media Format:

Graphics/Photos, Text/HTML, Downloadable docs

Technical Requirements:

Adobe Acrobat

Conditions of Use:

Creative Commons Attribution-Noncommercial-Share Alike 2.5