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Probability and Statistics

Probabilistic Learning: Theory and Algorithms  
Description: The course will provide a tutorial introduction to the basic principles of probabilistic modeling and then demonstrate the application of these principles to the analysis, development, and practical use of machine learning algorithms. Topics covered will include probabilistic modeling, defining likelihoods, parameter estimation using likelihood and Bayesian techniques, probabilistic approaches to classification, clustering, regression, and related topics such as model selection, bias/variance, and density estimation.
Course material created by Professor Padhraic Smyth.
Target audience: Graduate
Institution: University of California, Irvine
Materials available: Problem sets or projects, Textbook recommendations
Products: MATLAB

Submitted: Aug 19, 2008
Stat / Math Center  

MATLAB overview and support information from Indiana University's Stat/Math Center.

Language: English


Submitted: Sep 13, 2007
Statistics for Atmospheric and Oceanic Sciences  
Description: This is a graduate level course in statistical methods frequently used to interpret model results and observations in earth sciences. Topics covered include correlations and significance; linear regressions; empirical orthogonal functions; and uncertainty estimates from error propagation, Monte Carlo, and Boot Strap methods.
Course material created by Professor Sara Mikaloff Fletcher and Andrew Jacobson.
Target audience: Graduate
Institution: Princeton University
Materials available: Presentations, Downloadable code or data files
Products: MATLAB

Submitted: Aug 19, 2008
Probability and Statistical Inference  
Description: In this course we apply the mathematical techniques of probability to estimation and hypothesis testing, the formal methods by which we learn from noisy data, random samples, and other such uncertain real-world measurements. We culminate with linear regression, and introduce the powerful framework of Bayesian inference.
Course material created by Professor Alex Barnett.
Target audience: Advanced undergraduate (3rd or 4th year)
Institution: Dartmouth College
Materials available: Problem sets or projects, Course outline or syllabus, Textbook recommendations
Products: MATLAB

Submitted: Aug 06, 2008
Statistics for Applications  
Description: This course offers a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, and correlation.
Course material created by Professor Dmitry Panchenko.
Target audience: Advanced undergraduate (3rd or 4th year)
Institution: Massachusetts Institute of Technology
Materials available: Problem sets or projects, Course outline or syllabus, Textbook recommendations, Downloadable code or data files
Products: MATLAB

Submitted: Jul 22, 2008



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