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CMU CS 10708 - Speech recognition

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1 Introduction Graphical Models – 10708 Carlos Guestrin Carnegie Mellon University September 8th, 2008 Readings: Review: K&F: *2.1*, 2.2, 2.3 K&F: 3.1 10-708 – ©Carlos Guestrin 2008 2 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… 2 10-708 – ©Carlos Guestrin 2008 3 Speech recognition Hidden Markov models and their generalizations 10-708 – ©Carlos Guestrin 2008 4 Tracking and robot localization [Fox et al.] [Funiak et al.] Kalman Filters3 10-708 – ©Carlos Guestrin 2008 5 Evolutionary biology [Friedman et al.] Bayesian networks 10-708 – ©Carlos Guestrin 2008 6 Modeling sensor data Undirected graphical models [Guestrin et al.]4 10-708 – ©Carlos Guestrin 2008 7 Planning under uncertainty F’ E’ G’ P’ Peasant Footman Enemy Gold R t t+1 Time APeasant ABuild AFootman P(F’|F,G,AB,AF) [Guestrin et al.] Dynamic Bayesian networks Factored Markov decision problems 10-708 – ©Carlos Guestrin 2008 8 Images and text data [Barnard et al.] Hierarchical Bayesian models5 10-708 – ©Carlos Guestrin 2008 9 Structured data (text, webpages,…) [Koller et al.] Probabilistic relational models 10-708 – ©Carlos Guestrin 2008 10 And many many many many many more…6 10-708 – ©Carlos Guestrin 2008 11 Syllabus  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 2008 12 Prerequisites  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”7 10-708 – ©Carlos Guestrin 2008 13 Review 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 2008 14 Staff  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, x8-55278 10-708 – ©Carlos Guestrin 2008 15 First 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:  10708-announce@cs  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-f08 10-708 – ©Carlos Guestrin 2008 16 Text 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.9 10-708 – ©Carlos Guestrin 2008 17 Grading  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 2008 18 Homeworks  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 may discuss the questions  Each student writes their own answers  Write on your homework anyone with whom you collaborate  IMPORTANT:  We may use some material from previous years or from papers for the homeworks. Unless otherwise specified, please only look at the readings when doing your homework ! You are taking this advanced graduate class because you want to learn, so this rule is self-enforced 10 10-708 – ©Carlos Guestrin 2008 19 Enjoy!  NO CLASS THIS WEDNESDAY 9/10  Probabilistic graphical models are having significant impact in science, engineering and beyond  This class should give you the basic foundation for applying GMs and developing new methods  The fun begins… 10-708 – ©Carlos Guestrin


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CMU CS 10708 - Speech recognition

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