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Probability and Statistics
| Probabilistic Learning: Theory and Algorithms |
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| 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
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| Stat / Math Center |
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MATLAB overview and support information from Indiana University's Stat/Math Center.
Language: English
Submitted: Sep 13, 2007
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| Statistics for Atmospheric and Oceanic Sciences |
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| 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
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| Probability and Statistical Inference |
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| 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
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| Statistics for Applications |
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| 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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