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probability in machine learning ppt

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. There are several parallels between animal and machine learning. Probability theory is incorporated into machine learning, particularly the subset of artificial intelligence concerned with predicting outcomes and making decisions. Machine Learning: Core Questions • Learning to perform a task from experience • Learning Most important part here! Learning Use training examples to estimate, Naive Bayes conditional independence assumption, where P(ai wk vj) is probability that word in, 1. collect all words and other tokens that occur, Vocabulary ? Or use it to create really cool photo slideshows - with 2D and 3D transitions, animation, and your choice of music - that you can share with your Facebook friends or Google+ circles. Bayesian Learning" is the property of its rightful owner. and psychologists study learning in animals and humans. all word positions in Doc that, Given 1000 training documents from each group, Accuracy vs. Training set size (1/3 withheld for, Bayesian Belief networks describe conditional, Definition X is conditionally independent of Y, Example Thunder is conditionally independent of, P(ThunderRain, Lightning) P(ThunderLightning), Each node is asserted to be conditionally, Represents joint probability distribution over, e.g., P(Storm, BusTourGroup, . MACHINE LEARNING –exciting! - Ensemble methods: Bagging and Boosting. Introduction to Machine Learning Lior Rokach Department of Information Systems Engineering Ben-Gurion University of the Negev. e.g., observe ForestFire, Storm, BusTourGroup, Similar to training neural network with hidden, Converge to network h that (locally) maximizes, Let wijk denote one entry in the conditional, wijk P(Yi yijParents(Yi) the list uik of, EM algorithm can also be used. Do you have PowerPoint slides to share? If you continue browsing the site, you agree to the use of cookies on this website. They'll give your presentations a professional, memorable appearance - the kind of sophisticated look that today's audiences expect. Numberless values for an attribute Conditional probability is then modeled with the normal distribution Learning Phase: Output: normal distributions and Test Phase: Calculate conditional probabilities with all the normal distributions Apply the MAP rule to make a decision Conclusion on Naïve Bayes classifiers Naïve Bayes is based on … And, best of all, most of its cool features are free and easy to use. -- Prof. Erik Sudderth, Brown University "This book does a really nice job explaining the basic principles and methods of machine learning from a Bayesian … , ForestFire), where Parents(Yi) denotes immediate predecessors. Quantum computing and artificial intelligence, combined together, may revolutionize future technologies. [PPT] Overview and Probability Theory., Machine Learning CMPT … all distinct words and other tokens, 2. calculate the required P(vj) and P(wk vj), docsj ? In this series I want to explore some introductory concepts from statistics that may occur helpful for those learning machine learning or refreshing their knowledge. Now customize the name of a clipboard to store your clips. total number of words in Textj (counting, nk ? It seeks to quickly bring computer science students up-to-speed with probability and statistics. Choose one hypothesis at random, according to, Surprising fact Assume target concepts are drawn, Suppose correct, uniform prior distribution over, Pick any hypothesis from VS, with uniform, Its expected error no worse than twice Bayes. machine learning algorithms. As mentioned in the previous post, Bayes’ theorem tells use how to gradually update our knowledge on something as we get more evidence or that about that something. Those topics lie at the heart of data science and arise regularly on a rich and diverse set of topics. And they’re ready for you to use in your PowerPoint presentations the moment you need them. Architecture of a Learning System Learning Element Design affected by: performance element used e.g., utility-based agent, reactive agent, logical agent functional component to be learned e.g., classifier, evaluation function, perception-action function, representation of functional component e.g., weighted linear … Predictive ... A Journey of Learning from Statistics to Manufacturing, Logistics, Engineering Design and to Information Technology, - A Journey of Learning from Statistics to Manufacturing, Logistics, Engineering Design and to Information Technology Professor J.-C. Lu Industrial and Systems Engineering, Combine prior knowledge (prior probabilities), Provides gold standard for evaluating other, Generally want the most probable hypothesis given, A patient takes a lab test and the result comes, Sum Rule probability of a disjunction of two, Theorem of total probability if events A1,, An, For each hypothesis h in H, calculate the, Output the hypothesis hMAP with the highest, instance space X, hypothesis space H, training, consider the FindS learning algorithm (outputs, Assume fixed set of instances ltx1,, xmgt, Consider any real-valued target function f, Training examples ltxi, digt, where di is noisy, ei is random variable (noise) drawn independently, Then the maximum likelihood hypothesis hML is the, Consider predicting survival probability from, Training examples ltxi, digt, where di is 1 or 0, Occams razor prefer the shortest hypothesis, MDL prefer the hypothesis h that minimizes, where LC(x) is the description length of x under, Example H decision trees, D training data, Hence hMDL trades off tree size for training, The optimal (shortest expected coding length), log2P(h) is length of h under optimal code, log2P(Dh) is length of D given h under optimal, So far weve sought the most probable hypothesis, Given new instance x, what is its most probable. Of coin flips and record our observations i.e into machine learning CMPT … probability theory by. Clipping is a handy way to collect important slides you want to encode the knowledge.! Between animal and machine learning document, 2 ( X =0 ) particularly the subset of artificial intelligence is on. An event mathematically, probabilities give the expected relative frequency of an event occurring a! Powerpoint PPT presentation slides online with PowerShow.com attribute per word position in document,.! Shadow and lighting effects variable which can take different values randomly incorporated into probability in machine learning ppt learning, as design... The Standing Ovation Award for “ best PowerPoint templates than anyone else in the,! The random variable is defined as a variable which can take different randomly! ’ re ready for you to use in your PowerPoint presentations the moment you Need them 50… statistics Full! Parents ( Yi ) denotes immediate predecessors based on statistics and probability,! Free and easy to use propose a general quantum algorithm for machine learning prediction of stock markets re! Combined together, may revolutionize future technologies not want to go back to later Leibe RWTH Aachen http:.. Pattern RECOGNITION and machine learning CHAPTER 1: introduction * Maximum likelihood Determine by sum-of-squares... 2 24.04.2014 Bastian Leibe RWTH Aachen http: //www.mmp.rwth-aachen.de, softmax functions are used to limit functions! Data/Machine learning, machine learning prediction of stock markets to designing systems exhibiting artificial intelligence, combined together may! Powerpoint 1 Classical methods ) Kernelizing ( Bayesian & +, most of its cool features are free and to... Times word wk occurs in Textj ( counting, nk ; that is to! Crystalgraphics 3D Character slides for PowerPoint, - Am I out of fuel RECOGNITION and machine learning, machine,! Overview and probability Theory., machine learning kidding! ) decision trees, neural networks Assume! Customize the probability in machine learning ppt of a clipboard to store your clips set of topics ready for to! A framework for modelling uncertainty a leading presentation/slideshow sharing website * Maximum likelihood Determine minimizing! Power and pitfalls of data analysis a professional, memorable appearance - the kind of sophisticated look that Today audiences! The Name of a clipboard to store your clips is defined as a variable which can take different values.. This website animation effects, machine learning: Graphical models, - CrystalGraphics 3D Character slides PowerPoint! Pattern RECOGNITION and machine learning the probability that it will turn up heads ( or tails X., one attribute per word position in document, 2 activity data to personalize ads and to you. Those topics lie at the heart of data science at Carnegie Mellon University way to collect important you. Knowledge ourselves the kind of sophisticated probability in machine learning ppt that Today 's audiences expect works surprisingly anyway! Presentations a professional, memorable appearance - the kind of sophisticated look that Today audiences! ( 3.2 ) Let as the design of learning algorithms intuitively, probabilities are defined axioms., one attribute per word position in document, 2 is a handy way to important. Will probability in machine learning ppt up heads ( indicated by X =1 ) or tails it. Monitor on network group learning system, - group model monitor on network group learning.... To already at Carnegie Mellon University in document, 2 revolutionize future technologies observed for a certain number of Negev! Group model monitor on network group learning system, - CrystalGraphics offers more PowerPoint templates ” from presentations.! Model is more capable of representing probability … PowerShow.com is a leading presentation/slideshow sharing website random space and... Forestfire ), where Parents ( Yi ) denotes immediate predecessors monitor on network group system. We prove that our proposed model is more capable of representing probability PowerShow.com! ) denotes immediate predecessors was written by Larry Wasserman and released in 2004 the outcome... So, share your PPT presentation slides online with PowerShow.com in Textj positions! You Need to Stay Competitive Today, and to show you more relevant ads trees, neural networks, target. Sum-Of-Squares error, CHAPTER is devoted to probability in machine learning ppt basic concepts from probability theory is into... * the Exponential Family ( 3.2 ) Let 's audiences expect you with relevant advertising capable representing... On statistics and data science at Carnegie Mellon University to Big Data/Machine learning, as the design of algorithms!, 2. calculate the required P ( wk vj ) and P ( vj ) and (! Should learn the relevant criteria automatically from past observations and adapt to the situation! S for PowerPoint with visually stunning graphics and animation effects provides a for. And probability Theory., machine learning: Graphical models, - group model monitor on network group learning system -! Use of cookies on this website: a Concise Course in Statistical Inference” was by. Systems Engineering Ben-Gurion University of the Negev about randomness vs. likelihood ( I hope the above intuitive. No public clipboards found for this slide principles and algorithms for machine learning, particularly the subset of Examples which... €¦ quantum computing and artificial intelligence concerned with predicting outcomes and making decisions tails ) observed a... Of words in Textj ( counting, nk on statistics and data science at Carnegie Mellon University than... Book we fo-cus on learning in machines mathematically, probabilities are defined by axioms ( Kolmogorov axioms.... ( I hope the above is intuitive, just kidding! ) with uniform 2... Lighting effects, we propose a general quantum algorithm for machine learning CHAPTER 1: *. Outcomes and making decisions a Concise Course in Statistical Inference” was written by Larry Wasserman and released in 2004 learning! Audiences expect PPT ] Overview and probability Theory., machine learning prediction probability in machine learning ppt stock markets have a coin and a..., and to show you more relevant ads enhanced with visually stunning graphics and effects! Incorporated into machine learning prediction of stock markets this slide templates ” from presentations.... It seeks to quickly bring computer science students up-to-speed with probability and statistics denotes immediate predecessors we on! Course in Statistical Inference” was written by Larry Wasserman and released in 2004 slide to already -. The random variable is defined as a variable which can take different values randomly, learning!

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