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CMU CS 10708 - Introduction

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IntroductionSlide 2Speech recognitionTracking and robot localizationEvolutionary biologyModeling sensor dataPlanning under uncertaintyImages and text dataStructured data (text, webpages,…)Slide 10SyllabusPrerequisitesReview SessionsStaffFirst Point of Contact for HWsText BooksGradingHomeworksEnjoy!What are the fundamental questions of graphical models?More details???Where do we start?TodayRandom variableInterpretations of probability – A can of worms!Conditional probabilitiesTwo of the most important rules of the semester: 1. The chain ruleTwo of the most important rules of the semester: 2. Bayes ruleMost important concept: a) IndependenceMost important concept: b) Conditional independenceJoint distribution, MarginalizationMarginalization – The general caseBasic concepts for random variablesConditionally independent random variablesProperties of independenceBayesian networksHandwriting recognitionHandwriting recognition 2Webpage classificationWebpage classification 2Let’s start on BNs…What if variables are independent?Conditional parameterization – two nodesConditional parameterization – three nodesThe naïve Bayes model – Your first real Bayes NetWhat you need to knowNext classIntroductionGraphical Models – 10708Carlos GuestrinCarnegie Mellon UniversitySeptember 8th, 2008Readings:Review: K&F: *2.1*, 2.2, 2.3K&F: 3.110-708 – Carlos Guestrin 20082 One of the most exciting developments in machine learning (knowledge representation, AI, EE, Stats,…) in the last two (or three, or more) decades…My expectations are already high… 10-708 – Carlos Guestrin 20083Speech recognitionHidden Markov models and their generalizations10-708 – Carlos Guestrin 20084Tracking and robot localization[Fox et al.][Funiak et al.]Kalman Filters10-708 – Carlos Guestrin 20085Evolutionary biology[Friedman et al.]Bayesian networks10-708 – Carlos Guestrin 20086Modeling sensor dataUndirected graphical models[Guestrin et al.]10-708 – Carlos Guestrin 20087Planning under uncertaintyF’E’ G’ P’ PeasantFootman Enemy GoldRt t+1TimeAPeasant ABuild AFootman P(F’|F,G,AB,AF)[Guestrin et al.]Dynamic Bayesian networksFactored Markov decision problems10-708 – Carlos Guestrin 20088Images and text data[Barnard et al.]Hierarchical Bayesian models10-708 – Carlos Guestrin 20089Structured data (text, webpages,…)[Koller et al.]Probabilistic relational models10-708 – Carlos Guestrin 200810And manymany many many manymore…10-708 – Carlos Guestrin 200811SyllabusCovers a wide range of Probabilistic Graphical Models topics – from basic to state-of-the-artYou will learn about the methods you heard about:Bayesian networks, Markov networks, factor graphs, conditional random fields, decomposable models, junction trees, parameter learning, structure learning, semantics, exact inference, variable elimination, context-specific independence, approximate inference, sampling, importance sampling, MCMC, Gibbs, variational inference, loopy belief propagation, generalized belief propagation, Kikuchi, Bayesian learning, missing data, EM, Chow-Liu, structure search, IPF for tabular MRFs, Gaussian and hybrid models, discrete and continuous variables, temporal and template models, hidden Markov Models, Forwards-Backwards, Viterbi, Baum-Welch, Kalman filter, linearization, switching Kalman filter, assumed density filtering, DBNs, BK, Relational probabilistic models, Causality,…Covers algorithms, theory and applicationsIt’s going to be fun and hard work 10-708 – Carlos Guestrin 200812Prerequisites10-701 – Machine Learning, especially:Probabilities Distributions, densities, marginalization…Basic statisticsMoments, typical distributions, regression…AlgorithmsDynamic programming, basic data structures, complexity…ProgrammingMatlab will be very usefulWe provide some background, but the class will be fast pacedAbility to deal with “abstract mathematical concepts”10-708 – Carlos Guestrin 200813Review SessionsVery useful!Review materialPresent backgroundAnswer questionsThursdays, 5:00-6:20 in Wean Hall 5409First recitation is this ThursdayReview of probabilities & statisticsSometimes this semester: Especial recitations most likely on Mondays 5:30-7pmCover special topics that we can’t cover in classThese are optional, but you are here to learn… Do we need a Matlab review session?10-708 – Carlos Guestrin 200814StaffTwo Great TAs: Great resource for learning, interact with them!Amr Ahmed <[email protected]>, Dhruv Batra <[email protected]>Administrative AssistantMichelle Martin <[email protected]>, Wean 4619, x8-552710-708 – Carlos Guestrin 200815First Point of Contact for HWsTo facilitate interaction, a TA will be assigned to each homework question – This will be your “first point of contact” for this questionBut, you can always ask any of usFor e-mailing instructors, always use:[email protected]For announcements, subscribe to:[email protected]https://mailman.srv.cs.cmu.edu/mailman/listinfo/10708-announceWe will also use a discussion group:Post your questions, discuss projects, etcBe nice… Don’t give away any answers… http://groups.google.com/group/10708-f0810-708 – Carlos Guestrin 200816Text BooksPrimary: Daphne Koller and Nir Friedman, Structured Probabilistic Models, in preparation. These chapters are part of the course reader. You can purchase one from Michelle Martin Secondary: M. I. Jordan, An Introduction to Probabilistic Graphical Models, in preparation. Copies of selected chapters will be made available.10-708 – Carlos Guestrin 200817Grading5 homeworks (50%)First one goes out next Wednesday!Homeworks are long and hard please, please, please, please, please, please start early!!!Final project (30%)Done individually or in pairsDetails out soonProposals due October 6th Final (20%)Take home, out Dec. 3rdDue Dec. 10th at NOON (hard deadline)10-708 – Carlos Guestrin 200818HomeworksHomeworks are hard, start early Due in the beginning of class3 late days for the semesterAfter late days are used up:Half credit within 48 hoursZero credit after 48 hoursAll homeworks must be handed in, even for zero creditLate homeworks handed in to Michelle Martin, WEH 4619CollaborationYou


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