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• Weka
  ...ules, and visualization. It is also well-suited for developing new machine learning schemes. ...cs.waikato.ac.nz/ml/weka/ Weka ] web site. It was developed by the Machine Learning Group at University of Waikato in New Zealand.
  540 bytes (85 words) - 21:09, 26 September 2012
• Class meeting for 10-605 2012 02 28
  ...h Large Datasets 10-605 in Spring 2012|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2012]]. * Randomized algorithms (Bloom filters and LSH); map-reduce version of PageRank
  850 bytes (118 words) - 15:02, 5 March 2012
• Class meeting for 10-605 2013 02 25
  ...h Large Datasets 10-605 in Spring 2013|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2013]]. * [http://dl.acm.org/citation.cfm?id=1219840.1219917 Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Cluste
  750 bytes (102 words) - 17:15, 8 January 2014
• Class meeting for 10-605 in Fall 2016 Probability Review
  ...ith Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2016]]. * What are streaming machine learning algorithms: ML algorithms that never load in the data
  949 bytes (131 words) - 16:02, 10 August 2017
• Class meeting for 10-605 Probability Review
  ...ith Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2017]]. * What are streaming machine learning algorithms: ML algorithms that never load in the data
  874 bytes (123 words) - 17:46, 30 August 2017
• Class meeting for 10-605 SSL on Graphs
  ...ith Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2017]]. ...mmer (2009):] New regularized algorithms for transductive learning Machine Learning and Knowledge Discovery in Databases, 442-457
  2 KB (214 words) - 12:20, 14 November 2017
• Class meeting for 10-405 Probability Review
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. * What are streaming machine learning algorithms: ML algorithms that never load in the data
  945 bytes (137 words) - 14:27, 24 January 2018
• Class meeting for 10-405 SSL on Graphs
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ...mmer (2009):] New regularized algorithms for transductive learning Machine Learning and Knowledge Discovery in Databases, 442-457
  2 KB (231 words) - 10:50, 30 March 2018
• Class meeting for 10-605 2013 04 01
  ...h Large Datasets 10-605 in Spring 2013|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2013]]. ...p://jmlr.csail.mit.edu/papers/volume10/newman09a/newman09a.pdf Distributed Algorithms for Topic Models], Newman et al, JMLR 2009.
  745 bytes (107 words) - 17:18, 8 January 2014
• Class meeting for 10-605 2012 04 10
  ...h Large Datasets 10-605 in Spring 2012|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2012]]. ...p://jmlr.csail.mit.edu/papers/volume10/newman09a/newman09a.pdf Distributed Algorithms for Topic Models], Newman et al, JMLR 2009.
  808 bytes (119 words) - 17:04, 10 April 2012
• Class meeting for 10-605 Parameter Servers
  ...ith Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2017]]. ...w.cs.cmu.edu/~feixia/files/ps.pdf Parameter Server for Distributed Machine Learning]
  1 KB (174 words) - 11:37, 28 November 2017
• Winnow Algorithm
  Winnow Algorithm is a [[category::method | ]] for learning a linear classifier/decision hyper-plane from labeled examples. It scales w ...n Irrelevant Attributes Abound: A New Linear-threshold Algorithm", Machine Learning]
  635 bytes (79 words) - 06:09, 6 November 2012
• Class meeting for 10-405 Parameter Servers
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ...w.cs.cmu.edu/~feixia/files/ps.pdf Parameter Server for Distributed Machine Learning]
  1 KB (174 words) - 15:29, 15 January 2018
• Class meeting for 10-605 Randomized
  ...ith Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]]. * [http://dl.acm.org/citation.cfm?id=1219840.1219917 Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Cluste
  1 KB (223 words) - 16:28, 11 August 2016
• Class Meeting for 10-710 09-29-2011
  ...ds for Structured and Interdependent Output Variables]. Journal of Machine Learning Research 6:1453–1484. ...ith 2011]; also, A.5 (in the appendix) discusses "aggressive" optimization algorithms
  2 KB (261 words) - 19:23, 28 September 2011
• Class meeting for 10-605 SGD for MF 2 and Randomized Algorithms
  ...h Large Datasets 10-605 in Spring 2014|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2014]]. * [http://www.cs.cmu.edu/~wcohen/10-605/randomized-algs.pptx Randomized Algorithms - Slides]
  877 bytes (123 words) - 11:22, 26 February 2014
• Class meeting for 10-605 Graphs 2
  ...h Large Datasets 10-605 in Spring 2014|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2014]]. * [http://www.umiacs.umd.edu/~amit/Papers/goyalPointQueryEMNLP12.pdf Sketch Algorithms for Estimating Point Queries in NLP.] Amit Goyal, Hal Daume III, and Graha
  936 bytes (135 words) - 09:15, 28 April 2014
• Class meeting for 10-605 Scalable PageRank
  ...ith Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]]. ...p], [http://www.cs.cmu.edu/~wcohen/10-605/randomized-algs-2.pdf Randomized Algorithms PDF version].
  2 KB (321 words) - 13:31, 10 August 2016
• Class meeting for 10-605 2013 02 18
  ...h Large Datasets 10-605 in Spring 2013|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2013]]. * [http://dl.acm.org/citation.cfm?id=1219840.1219917 Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Cluste
  1,010 bytes (140 words) - 17:13, 8 January 2014
• NIPS 1988-1999
  ...cognitive science (CS), control and navigation (CN), implementations (IM), learning theory (LT), neuroscience (NS), signal processing (SP), vision sciences (VS
  501 bytes (71 words) - 16:24, 31 March 2011
• 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
• 10-601 Sequences
  This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] * What the Viterbi and forward-background algorithms are:
  1 KB (181 words) - 15:16, 21 April 2016
• K-means
  ...the data as well as in the iterative refinement approach employed by both algorithms.
  1 KB (190 words) - 00:02, 28 March 2011
• Machine Learning with Large Datasets 10-605 in Spring 2013
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI ** a machine learning course (e.g., 10-701 or 10-601). You may take this concurrently with the i
  5 KB (715 words) - 12:07, 26 April 2013
• Project dong, 10-802 spring 2010
  The following is an overview of techniques using '''machine learning techniques''' ..._classifier_learning | Naive Bayes]] & [[Support vector machine classifier learning | SVM]]
  3 KB (397 words) - 17:01, 1 February 2011
• The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity
  ...ch they used 2000 papers categorized into one of the sub fields of Machine learning. * '''PHITS''' - D. Cohn and H. Chang. Learning to probabilistically identify authoritative documents. In
  4 KB (610 words) - 17:08, 5 November 2012
• Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs
  title={Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs}, journal={Machine Learning and Knowledge Discovery in Databases},
  3 KB (495 words) - 13:26, 5 November 2012
• Machine Learning with Large Datasets 10-605 in Spring 2012
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI ** a machine learning course (e.g., 10-701 or 10-601). You may take this concurrently with the i
  5 KB (716 words) - 11:34, 1 May 2012
• Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016
  This is the syllabus for [[Machine Learning with Large Datasets 10-605 in Fall 2016]]. ...lable classification, Scalable Rocchio and TFIDF, Abstracts for map-reduce algorithms, Joins in Hadoop, TFIDF in Pig, Guinea Pig intro, TFIDF in Guinea Pig
  7 KB (1,002 words) - 11:54, 11 August 2017
• 10-601 Wrap-up on Linear Classification
  This a lecture used in the [[Syllabus for Machine Learning 10-601]] To summarize this part of the course, we looked at a four algorithms in some detail.
  1 KB (225 words) - 16:52, 30 July 2013
• Class Meeting for 10-710 10-27-2011
  * [http://aclweb.org/anthology-new/J/J98/J98-4003.pdf Machine Transliteration], K. Knight and J. Graehl, CL 1998 ...rs on WFST algorithms are indexed [http://www.cs.nyu.edu/~mohri/transducer-algorithms.html here]
  2 KB (279 words) - 16:31, 27 October 2011

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