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• 10-601B Boosting and Other Ensembles
  This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] * [http://dl.acm.org/citation.cfm?id=743935 Ensemble Methods in Machine Learning], Tom Dietterich
  1,006 bytes (139 words) - 10:18, 12 January 2016
• 10-601 Ensembles 2
  This a lecture used in the [[Syllabus for Machine Learning 10-601 in Fall 2014]] * [http://dl.acm.org/citation.cfm?id=743935 Ensemble Methods in Machine Learning], Tom Dietterich
  1 KB (188 words) - 17:36, 21 July 2014
• Class meeting for 10-605 Subsampling a Graph
  ...ith Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]]. ..., and Kevin Lang. "Local partitioning for directed graphs using PageRank." Algorithms and Models for the Web-Graph. Springer Berlin Heidelberg, 2007. 166-178.]
  2 KB (230 words) - 16:44, 1 August 2017
• 10-601 Ensembles
  This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] * [http://dl.acm.org/citation.cfm?id=743935 Ensemble Methods in Machine Learning], Tom Dietterich
  1 KB (181 words) - 16:45, 6 January 2016
• Class meeting for 10-605 GraphLab
  ...ith Large Datasets 10-605 in Fall 2015|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2015]]. ...tp://www.cs.cmu.edu/~wcohen/postscript/iswc-2010.pdf Signal/Collect: Graph Algorithms for the (Semantic) Web] in ISWC-2010. [http://www.cs.cmu.edu/~wcohen/10-60
  1 KB (176 words) - 18:33, 6 December 2015
• Class meeting for 10-605 Randomized Algorithms
  ...ith Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2017]]. ...u.edu/~wcohen/10-605/notes/randomized-algs.pdf lecture notes on randomized algorithms].
  3 KB (406 words) - 11:34, 28 November 2017
• Class meeting for 10-405 Randomized Algorithms
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ...u.edu/~wcohen/10-605/notes/randomized-algs.pdf lecture notes on randomized algorithms] (covering Bloom filters and countmin sketches).
  3 KB (431 words) - 10:43, 23 April 2018
• Class meeting for 10-605 Workflows For Hadoop
  ...ith Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2017]]. ...r Text Categorization]. Proceedings of International Conference on Machine Learning (ICML), 1997.
  3 KB (434 words) - 12:37, 19 September 2017
• Class meeting for 10-405 Workflows For Hadoop
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ...r Text Categorization]. Proceedings of International Conference on Machine Learning (ICML), 1997.
  3 KB (420 words) - 11:12, 5 March 2018
• Class meeting for 10-605 LDA
  ...ith Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]]. ..., and Michael I. Jordan. "Latent Dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.
  2 KB (296 words) - 11:31, 20 November 2017
• Class meeting for 10-405 LDA
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ..., and Michael I. Jordan. "Latent Dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.
  2 KB (315 words) - 10:23, 16 April 2018
• Multi-class Classification
  In machine learning, multiclass or multinomial classification is the problem of classifying ins ...rally permit the use of more than two classes, others are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of
  1 KB (210 words) - 07:56, 2 October 2012
• Class Meeting for 10-710 09-22-2011
  ...g Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms, Collins, EMNLP 2002]. ...= A.| last = Globerson| pages = 305–312| title = Exponentiated gradient algorithms for log-linear structured prediction}}]]. A more recent EG-based approach.
  2 KB (291 words) - 16:39, 22 September 2011
• Class Meeting for 10-707 10/6/2010
  ...g Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms, Collins, EMNLP 2002]. ...= A.| last = Globerson| pages = 305–312| title = Exponentiated gradient algorithms for log-linear structured prediction}}]]. A more recent EG-based approach.
  2 KB (279 words) - 10:22, 4 October 2010
• Daume et al, ML 2009
  ...gford, J., and Marcu, D. 2009. Search-based structured prediction. Machine Learning. 75. 3. p297-325 ...oduces [[UsesMethod::SEARN]], a meta-algorithm that combines searching and learning to make structured predictions. Note that this is the journal version of th
  1 KB (163 words) - 18:22, 30 September 2010
• Machine Learning with Large Datasets 10-605
  * Prerequisites: a machine learning course (e.g., 10-701 or 10-601) must be taken either before, or concurrentl * Sample syllabus: [[Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012]]
  5 KB (671 words) - 12:55, 14 November 2011
• Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012
  ...taking 10-605 now, you're probably looking for the syllabus for [[Machine Learning with Large Datasets 10-605 in Spring 2013]].''' * Tues Jan 24. [[Class meeting for 10-605 2012 01 24|Streaming algorithms and Naive Bayes.]]
  5 KB (683 words) - 09:48, 28 March 2013
• Class meeting for 10-405 Parallel Perceptrons
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. * Parallellizing streaming ML algorithms
  2 KB (308 words) - 11:28, 6 March 2018
• Information Extraction 10-707 in Fall 2010
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI * Prerequisites: a machine learning course (e.g., 10-701 or 10-601) or consent of the instructor.
  4 KB (620 words) - 10:40, 20 September 2010
• 10-601 Clustering
  This a pair of lectures used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]]. * K-means algorithms
  997 bytes (149 words) - 16:49, 6 January 2016

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