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 February 23, 2015 Today: • Graphical models • Bayes Nets: • Representing distributions • Conditional independencies • Simple inference • Simple learning Readings: • Bishop chapter 8, through 8.2 • Mitchell chapter 6 Bayes Nets define Joint Probability Distribution in terms of this
Machine Learning and Differential Privacy Maria-Florina Balcan 04/22/2015 Learning and Privacy • To do machine learning, we need data. • What if the data contains sensitive information? • medical data, web search query data, salary data, student grade data. • Even if the (person running the) learning algo can be trusted, perhaps the output of
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CHAPTER 2 Estimating Probabilities Machine Learning Copyright ⃝c 2017. Tom M. Mitchell. All rights reserved. *DRAFT OF January 26, 2018* *PLEASE DO NOT DISTRIBUTE WITHOUT AUTHOR’S PERMISSION* This is a rough draft chapter intended for inclusion in the upcoming second edition of the textbook Machine Learning, T.M. Mitchell, McGraw Hill. You are welcome to use
10-601 Machine Learning Maria-Florina Balcan Spring 2015 Plan: Perceptron algorithm for learning linear separators. 1 Learning Linear Separators Here we can think of examples as being from {0,1}n or from Rn. Given a training set of labeled examples (that is consistent with a linear separator),we can find a hyperplane w · x = w0 such
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Boosting Approach to ML Perceptron, Margins, Kernels Maria-Florina Balcan 03/18/2015 • • Recap from last time: Boosting General method for improving the accuracy of any given learning algorithm. Works by creating a series of challenge datasets s.t. even modest performance on these can be used to produce an overall high-accuracy predictor. Adaboost one of the
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Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University February 4, 2015 Today: • Generative – discriminative classifiers • Linear regression • Decomposition of error into bias, variance, unavoidable Readings: • Mitchell: “Naïve Bayes and Logistic Regression” (required) • Ng and Jordan paper (optional) • Bishop, Ch 9.1, 9.2 (optional) Logistic Regression
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Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University April 15, 2015 Today: • Artificial neural networks • Backpropagation • Recurrent networks • Convolutional networks • Deep belief networks • Deep Boltzman machines Reading: • Mitchell: Chapter 4 • Bishop: Chapter 5 • Quoc Le tutorial: • Ruslan Salakhutdinov tutorial: Artificial Neural
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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 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University Today: • TheBigPicture • Overfitting • Review:probability January 14, 2015 Readings: Decisiontrees,overfiting • Mitchell,Chapter3 Probability review • BishopCh.1thru1.2.3 • Bishop,Ch.2thru2.2 • AndrewMoore’sonline tutorial Function Approximation: Problem Setting: • SetofpossibleinstancesX • Unknowntargetfunctionf:XàY • SetoffunctionhypothesesH={h|h:XàY} Input: • Trainingexamples{}ofunknowntargetfunctionf Output: • Hypothesish∈Hthatbestapproximatestargetfunctionf Function Approximation: Decision Tree
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