Essentials of Probability and Statistical Inference IV: Algorithmic and Nonparametric Approaches
| Rating: | Not rated yet |
| Rate item | |
| Type: | Course Related Materials |
| Grade Level: | Post-secondary |
Author: Irizarry, Rafael
Subject: Science and Technology, Social Sciences, Mathematics and Statistics
Institution Name:
Johns Hopkins Bloomberg School of Public Health
Collection Name: JHSPH OpenCourseWare
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.
Details
Course Type: Full Course
Material Types: Lecture Notes, Syllabi
Media Formats: Text/HTML, Downloadable docs, Graphics/Photos
Language: English
Additional Information
Geographic
Regional Relevance: All
Notable Hardware, Software, and Network Requirements:
Adobe Acrobat
Adobe Acrobat
