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FALL 2006 COURSES
Topics in Bioinformatics, 550.635
A "readings" course organized around selected research articles in the recent bioinformatics and computational biology literatures. In this term, the choice of papers will favor work about extracting information from high throughput molecular data, such as inferring phenotype from genotype, and modeling regulatory networks, based on microarrays bearing the expression levels of thousands of gene transcripts or proteins. One major objective is to prepare students to comfortably read articles which involve extensive mathematical and statistical modeling as well as techniques from pattern recognition and machine learning. Some papers will be presented by the students and others by the instructor. In addition, expositions will be supplemented by lectures on various aspects of statistical learning, modeling and prediction, such as properly estimating generalization error in cancer classification and avoiding over-fitting in learning networks of molecular interactions.
Probability & Statistics for the Biological and Medical Sciences and Engineering, 550.311
An introduction to probability and statistics intended for students in biomedical fields who plan to take only one course in this area. The level and scope is similar to 550.310, except that in 550.311 there is an emphasis on examples and interpretations which arise in biology and medicine. Two semesters of calculus is a prerequisite, including single and multiple integrals. Topics include: combinatorics, random variables, probability distributions, independence, Bayes theorem, expectations, the normal density, random vectors and joint densities, the Central Limit Theorem, parameter estimation, confidence intervals, hypothesis testing, goodness-of-fit tests. Students wishing a more extensive treatment of this material are encouraged to consider 550.420-430 instead of 550.311.
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