Material Detail
Essentials of Probability and Statistical Inference IV: Algorithmic and NonParametic Approaches
This course 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. OCW offers a snapshot of the educational content offered by JHSPH. OCW materials are not for credit towards any degrees or certificates offered by the Johns Hopkins Bloomberg School of Public Health. Included here are a syllabus and lectures.
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More about this material
- Material Type:
- Online Course
- Date Added to MERLOT:
- July 18, 2008
- Date Modified in MERLOT:
- November 12, 2021
- Author:
- Rafael Irizarry, Johns Hopkins Bloomberg School of Public Health
- Submitter:
-
Cathy Swift
- Primary Audience:
- College Upper Division, Graduate School, Professional
- Technical Format:
- Mobile Compatibility:
- Not specified at this time
- Language:
- English
- Cost Involved:
- No
- Source Code Available:
- Unknown
- Accessibility Information Available:
- Unknown
- Creative Commons:
-
This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States
