PPT-Naïve

Bayes William W Cohen Probabilistic and Bayesian Analytics Andrew W Moore School of Computer Science Carnegie Mellon University wwwcscmueduawm awmcscmuedu 4122687599 Note to other teachers and users of these slides Andrew would be delighted if you found this source material useful in giving your own lectures Feel free to use these slides verbatim or to modify them to fit your own needs PowerPoint originals are available If you make use of a significant portion of these slides in your own lecture please include this message or the following link to the source repository of Andrews tutorials . John Braun University of Western Ontario Journal of Statistics Education Volume 20 Number 2 2012 httpwwwamstatorgpublicationsjsev20n2braunpdf Copyright 2012 by W John Braun all rights reserved This text may be freely shared among individuals but it Tom M Mitchell All rights reserved DRAFT OF January 19 2010 PLEASE DO NOT DISTRIBUTE WITHOUT AUTHORS PERMISSION This is a rough draft chapter intended for inclusion in a possible second edition of the textbook Machine Learn ing TM Mitchell McGraw H inten- intention: intention: intention: demanding liar mental planning reactions. liar a a H a a a a S S a S S phase, C a a content characteristics a a C C C S a a S d ELECTRON (Overview): 6 parts, 22 arms. Phase . 2. Treatment. Naïve and Treatment . Experienced. Source: . Gilead Sciences, . Inc. Sofosbuvir. Summary of ELECTRON Trials Design (1 of 2). Part 1 . - . Jim Demarest. ,. 1. Mark Underwood,. 2. Marty St Clair,. 2. . David Dorey,. 3. Steve Almond,. 3. Robert Cuffe,. 4. . Dannae Brown,. 5. Garrett Nichols. 6. . 1. ViiV Healthcare, Global R&D, Research Triangle Park, NC, USA; . Bayes. William W. Cohen. Probabilistic and Bayesian Analytics. Andrew W. Moore. School of Computer Science. Carnegie Mellon University. www.cs.cmu.edu/~awm. awm@cs.cmu.edu. 412-268-7599. Note to other teachers and users of these slides. Andrew would be delighted if you found this source material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. PowerPoint originals are available. If you make use of a significant portion of these slides in your own lecture, please include this message, or the following link to the source repository of Andrew’s tutorials: . Theparadigmisoftheoreticalinterestbecauseitshowsthatthereisafun-damentalalternativetothedominantapproachtoclassicationlearning.Thedominantapproachperformssearchthroughahypothesisspacetoidentifythehyp Using . MapReduce. Jacopo . Urbani. , Spyros . Kotoulas. ,. Eyal. Oren, and Frank van . Harmelen. Department of Computer Science,. Vrije. . Universiteit. Amsterdam,. The Netherlands . . . Naively, we would attempt batch proximal gradient descent on this objective function, which would involve the following steps: 1. Given current iterate θ , calculate current λ for al 2. Poisson compositing. 3. our result. 4. Objectives. Robust to inaccurate selection. Output Quality. - Limit color bleeding. Time-Performance. - . Efficient method. 5. Seamless compositing. Poisson Compositing Result . CD8+ Effector T Cells. DIFFERENTIATION OF CD8+ T CELLS INTO . CYTOTOXIC T . LYMPHOCYTES. Induction . and effector . phases of CD8+ . T cell . responses. Nature of Antigen and Antigen-Presenting . Cells for . SPRINT-2. Phase 3, Treatment . Naive. Treatment. . Naïve. Source: . Poordad F, . et al. . N Engl J Med. 2011;364:1195-206.. Boceprevir . for Treatment-Naïve HCV Genotype 1. SPRINT. -2 Trial: Study Design. Memorization methods. Linear . and. . logistic . regression. 1042. . Data . Science in . Practice. Week 12, 05/09. Jia-Ming Chang. http://. www.cs.nccu.edu.tw. /~. jmchang. /course/1042/. datascience. http://xkcd.com/1236/. Bayes. Rule. The product rule gives us two ways to factor . a joint probability:. Therefore,. Why is this useful?. Can update our beliefs about A based on evidence B. . P(A) is the .