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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
