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

• Author: Irizarry, Rafael
• Subject: Science and Technology, Social Sciences, Mathematics and Statistics
• 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.

• Course Type: Full Course
• Languages: English
• Material Types: Lecture Notes, Syllabi
• Media Formats: Text/HTML, Downloadable docs, Graphics/Photos
• Technical Requirements: Adobe Acrobat

• Conditions of Use: Creative Commons Attribution-Noncommercial-Share Alike 2.5