Reinforcement Learning Maria-Florina Balcan Carnegie Mellon University April 20, 2015 Readings: • Mitchell, chapter 13 • Kaelbling, et al., Reinforcement Learning: A Survey Today: • Learning of control policies • Markov Decision Processes • Temporal difference learning • Q learning Slides courtesy: Tom Mitchell Tom Mitchell, April 2011 Overview • Different from ML pbs so […]
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Semi-Supervised Learning Xiaojin Zhu, University of Wisconsin-Madison Synonyms: Learning from labeled and unlabeled data, transductive learn- ing Definition Semi-supervised learning uses both labeled and unlabeled data to perform an otherwise supervised learning or unsupervised learning task. In the former case, there is a distinction between inductive semi-supervised learning and transductive learning. In inductive semi-supervised learning,
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Two faces of active learning Sanjoy Dasgupta dasgupta@cs.ucsd.edu Abstract An active learner has a collection of data points, each with a label that is initially hidden but can be obtained at some cost. Without spending too much, it wishes to find a classifier that will accurately map points to labels. There are two common intuitions
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CHAPTER 3 GENERATIVE AND DISCRIMINATIVE CLASSIFIERS: NAIVE BAYES AND LOGISTIC REGRESSION Machine Learning Copyright ⃝c 2017. Tom M. Mitchell. All rights reserved. *DRAFT OF October 1, 2020* *PLEASE DO NOT DISTRIBUTE WITHOUT AUTHOR’S PERMISSION* This is a rough draft chapter intended for inclusion in the upcoming sec- ond edition of the textbook Machine Learning, T.M.
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Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 21, 2015 Today: • BayesRule • Estimatingparameters • MLE • MAP some of these slides are derived from William Cohen, Andrew Moore, Aarti Singh, Eric Xing, Carlos Guestrin. – Thanks! Readings: Probabilityreview • BishopCh.1thru1.2.3 • Bishop,Ch.2thru2.2 • AndrewMoore’sonline tutorial Announcements • Class
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Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University February 25, 2015 Today: • Graphical models • Bayes Nets: • Inference • Learning • EM Readings: • Bishop chapter 8 • Mitchell chapter 6 Midterm • In class on Monday, March 2 • Closed book • You may bring a 8.5×11 “cheat
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Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University March 4, 2015 Today: • Graphical models • Bayes Nets: • EM • Mixture of Gaussian clustering • Learning Bayes Net structure (Chow-Liu) Readings: • Bishop chapter 8 • Mitchell chapter 6 Learning of Bayes Nets • Fourcategoriesoflearningproblems – Graph structure may be
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Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 26, 2015 Today: • BayesClassifiers • ConditionalIndependence • NaïveBayes Readings: Mitchell: “Naïve Bayes and Logistic Regression” (available on class website) Two Principles for Estimating Parameters • MaximumLikelihoodEstimate(MLE):chooseθthat maximizes probability of observed data • MaximumaPosteriori(MAP)estimate:chooseθthat is most probable given prior probability and the
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Machine Learning Theory Maria-Florina (Nina) Balcan February 9th, 2015 A2 Â Goals of Machine Learning Theory Develop & analyze models to understand: • what kinds of tasks we can hope to learn, and from what kind of data; what are key resources involved (e.g., data, running time) • prove guarantees for practically successful algs (when
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Center Based Clustering: A Foundational Perspective Pranjal Awasthi and Maria-Florina Balcan Princeton University and Carnegie Mellon University November 10, 2014 Abstract In the first part of this chapter we detail center based clustering methods, namely methods based on finding a “best” set of center points and then assigning data points to their nearest center. In