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In the past five to ten years, there have been a number of significant developments in the fields of machine learning and statistical learning. These advances are having a great impact on applications in various areas of science and engineering, as well as on the science of statistical inference itself.
This short course covers three of the most promising developments: support-vector machines, boosting, and Bayesian networks. (The EM algorithm will also be discussed, in the context of Bayesian networks.) Two of these methods have begun to be applied in bioinformatics settings. The methods, as well as their published applications to bioinformatics problems, are presented. The course is designed for bioinformatics software developers, data analysts, machine learners, computer scientists, mathematicians, statisticians, and biologists with an interest in the topic.
The topics will be covered in the following sequence:
The material will be drawn from text book chapters as well as research papers on this topic. This list, and lecture notes, will be made available to enrollees, at the course's private web site.
The course will also disseminate (and exchange) information about current public domain or commercial software. Information will be disseminated at the course's private web site.
Data Analysis, Modeling, and Visualization for Bioinformatics or a recent course on Probability and Statistics or a course on Machine Learning.
There will be no mandatory assignments. Optional practice assignments (not to be turned in) will be posted to the course's private web site.