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DTSTART;TZID=America/New_York:20190204T090000
DTEND;TZID=America/New_York:20190204T130000
DTSTAMP:20190718T125329
CREATED:20180306T170900Z
LAST-MODIFIED:20180821T150444Z
UID:2089-1549270800-1549285200@calendar.brighamandwomens.org
SUMMARY:A Crash Course in Statistical Learning Methods\, Part II
DESCRIPTION:This course\, led by Olga Demler\, PhD and Franco Giulianini\, PhD\, will review the Machine Learning methods used in medical research. The material will be split over two days (see topic outlines below). Laptops are required. Please install the programs R and RStudio before the course. Please register as space is limited (HERE for Part 1\, and HERE for Part 2). \n \nObjectives: \n\nBecome familiar with the intuition behind each method and the language used in the field\nGain hands-on experience using these algorithms in the R programming language\n\nPrerequisites: \n\nWorking knowledge of intermediate statistical analysis including linear and logistic regressions and linear discriminant analysis\nTo participate in the practice exercises (which are optional)\, beginner-level proficiency with R programming language is required (an equivalent of completing weeks 1\, 2 and 3 of https://www.coursera.org/learn/r-programming — JHU “R programming” course on Coursera). Please bring a laptop with R and RStudio installed from www.r-project.org and www.rstudio.com.\n\n \nThis material is based on recent developments in the field (references will be provided) and the book by Friedman\, Hastie\, Tibshirani “The Elements of Statistical Learning” (http://statweb.stanford.edu/~tibs/ElemStatLearn). \n ———————————————————————————– \nTOPICS: Statistical Leaning Methods I (May 15th) – Register HERE \n\nSupervised Learning Algorithms\n\nClassification and Regression Trees\nRandom Forests\nSupport Vector Machines\n\n\nFeature Selection Algorithms\n\nLASSO\nRidge Regression\nElastic Net\n\n\nDeep Learning\n\nConvolutional Neural Networks\nRecursive Neural Networks\n\n\n\n \nTOPICS: Statistical Leaning Methods II (February 4th) – Register HERE \n\nUnsupervised Methods\n\nDimension Reduction\n\nPCA vs Factor analysis\ntSNE\n\n\nClassification and Pattern Recognition\n\nK means clustering\nK nearest neighbor classifier\n\n\n\n\nShort Review of Supervised Learning Methods: Elastic Net\, Random Forest\, SVM\nOther Topics:\n\nMultiple Comparison Techniques: controlling for family-wise type 1 error rate: Bonferroni\, FDR\, permutation-based FDR to adjust for correlated biomarkers\nCross-Validation\n\n\n\n
URL:http://calendar.brighamandwomens.org/event/a-crash-course-in-statistical-learning-methods-part-ii/
LOCATION:Bayles Conference Room – 15 Francis St.\, 15 Francis St. (C Elevator\, 3rd Floor)\, Boston\, MA\, 02115\, United States
CATEGORIES:Research & Research Education
ORGANIZER;CN="Brigham%20Research%20Education":MAILTO:BWHResearchEd@partners.org
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