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Block-Seminar "Classical Topics in Machine Learning"

Termine und Informationen

Erster Termin für Themenvergabe	Mittwoch, 16.11.2011, 10:00-12:00 Uhr, Raum FR 6046
Verantwortlich	Prof. Dr. Klaus-Robert Müller
Ansprechtpartner(in)	Paul von Bünau
Sprechzeiten	Nach Vereinbarung
Sprache	Englisch
Anrechenbarkeit	Wahlpflicht LV im Modul Maschinelles Lernen I (Informatik M.Sc.)

All information can be found in the ISIS course

Topics (tentative)

Paper(s)	Betreuer	Vortragender
Nonlinear Dimensionality Reduction by Locally Linear Embedding link Roweis, S. T. and Saul, L. K., 2000
Gaussian Processes - A Replacement for Supervised Neural Networks? link MacKay, D. J. C., 1997
Factor Graphs and the Sum-Product Algorithm link Kschischang, , Frey, and Loeliger, , 2001
Gaussian Processes in Machine Learning link Rasmussen, C. E., 2003
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition link Rabiner, L. R., 1989
Decoding by Linear Programming link Candes, and Tao, , 2005
Self-organizing formation of topologically correct feature maps Kohonen, T., 1982
Special Invited Paper. Additive Logistic Regression: A Statistical View of Boosting link Friedman, J., Hastie, T. and Tibshirani, R., 2000
Expectation Propagation for approximate Bayesian inference link Minka, T. P., 2001
A new look at the statistical model identification link Akaike, H., 1974
Error Correction via Linear Programming link Candes, , Rudelson, , Tao, and Vershynin, , 2005
A Global Geometric Framework for Nonlinear Dimensionality Reduction link Tenenbaum, J. B., de Silva, V. and Langford, J. C., 2000
An Introduction to MCMC for Machine Learning link Andrieu, , de Freitas, , Doucet, and Jordan, , 2003
Perspectives on Sparse Bayesian Learning link Wipf, D. P., Palmer, J. A. and Rao, B. D., 2003
Induction of decision trees link Quinlan, R., 1986
A Fast Learning Algorithm for Deep Belief Nets link Hinton, G. E., Osindero, S. and Teh, Y. W., 2006
How to Use Expert Advice link Cesa-Bianchi, , Freund, , Haussler, , Helmbold, , Schapire, and Warmuth, , 1997
A View of the EM Algorithm that Justifies Incremental, Sparse, and other Variants link Neal, R. and Hinton, G., 1998
Probabilistic Inference using Markov Chain Monte Carlo Methods link Neal, R. M., 1993
Model Selection Using the Minimum Description Length Principle link Bryant, P. G. and Cordero-Brana, O. I., 2000
Hierarchical Mixtures of Experts and the EM Algorithm link Jordan, M. I. and Jacobs, R. A., 1994
Gaussian Processes in Reinforcement Learning link Rasmussen, C. E. and Kuss, M., 2003
An introduction to variational methods for graphical models link Jordan, M. I., Ghahramani, Z. and Jaakkola, T. S., 1999

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Ensemble learning Induction of decision trees link Quinlan, R., 1986 Hierarchical Mixtures of Experts and the EM Algorithm link Jordan, M. I. and Jacobs, R. A., 1994
Spectral clustering A tutorial on spectral clustering link Von Luxburg, U., 2007
Expectation propagation Expectation Propagation for approximate Bayesian inference link Minka, T. P., 2001
Hidden Markov Models (HMM) A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition link Rabiner, L. R., 1989 A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains Baum, L., Petrie, T., Soules, G. and Weiss, N., 1970
Variational methods An introduction to variational methods for graphical models link Jordan, M. I., Ghahramani, Z. and Jaakkola, T. S., 1999
Learning bounds Tutorial on practical prediction theory for classification link Langford, J., 2006
Manifold learning Laplacian eigenmaps for dimensionality reduction and data representation Belkin, M. and Niyogi, P., 2003
Locally Linear Embedding (LLE) Nonlinear Dimensionality Reduction by Locally Linear Embedding link Roweis, S. T. and Saul, L. K., 2000
Random forests Random forests Breiman, L., 2001
Compressed sensing Decoding by Linear Programming link Candes, and Tao, , 2005 Error Correction via Linear Programming link Candes, , Rudelson, , Tao, and Vershynin, , 2005
Minimum description length (MDL) Model Selection Using the Minimum Description Length Principle link Bryant, P. G. and Cordero-Brana, O. I., 2000
Markov Chain Monte Carlo (MCMC) An Introduction to MCMC for Machine Learning link Andrieu, , de Freitas, , Doucet, and Jordan, , 2003 Probabilistic Inference using Markov Chain Monte Carlo Methods link Neal, R. M., 1993
Gaussian processes Gaussian Processes - A Replacement for Supervised Neural Networks? link MacKay, D. J. C., 1997 Gaussian Processes in Machine Learning link Rasmussen, C. E., 2003
Deep belief networks A Fast Learning Algorithm for Deep Belief Nets link Hinton, G. E., Osindero, S. and Teh, Y. W., 2006
Boosting Experiments with a new boosting algorithm link Freund, Y. and Schapire, R., 1996 Special Invited Paper. Additive Logistic Regression: A Statistical View of Boosting link Friedman, J., Hastie, T. and Tibshirani, R., 2000
Expectation Maximization (EM) Maximum likelihood from incomplete data via the EM algorithm Dempster, A., Laird, N. and Rubin, D., 1977 A View of the EM Algorithm that Justifies Incremental, Sparse, and other Variants link Neal, R. and Hinton, G., 1998
Message passing Factor Graphs and the Sum-Product Algorithm Kschischang, , Frey, and Loeliger, , 2001
Model selection A new look at the statistical model identification link Akaike, H., 1974
Kalman filters A new approach to linear filtering and prediction problems Kalman, R. and others, , 1960

IDA Wiki: Main/WS11_SeminarClassicalTopicsInML (last edited 2011-11-15 20:00:46 by PaulBuenau)

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|| Ensemble learning <<BR>>  Induction of decision trees [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.167.3624&rep=rep1&type=pdf|link]] <<BR>> Quinlan, R., 1986  Hierarchical Mixtures of Experts and the EM Algorithm [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.7391&rep=rep1&type=pdf|link]] <<BR>> Jordan, M. I. and Jacobs, R. A., 1994  || || ||
|| Spectral clustering <<BR>>  A tutorial on spectral clustering [[http://www.stanford.edu/class/ee378B/papers/luxburg-spectral.pdf|link]] <<BR>> Von Luxburg, U., 2007  || || ||
|| Expectation propagation <<BR>>  Expectation Propagation for approximate Bayesian inference [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.86.1319&rep=rep1&type=pdf|link]] <<BR>> Minka, T. P., 2001  || || ||
|| Hidden Markov Models (HMM) <<BR>>  A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.131.2084&rep=rep1&type=pdf|link]] <<BR>> Rabiner, L. R., 1989  A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains  <<BR>> Baum, L., Petrie, T., Soules, G. and Weiss, N., 1970  || || ||
|| Variational methods <<BR>>  An introduction to variational methods for graphical models [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.61.4999&rep=rep1&type=pdf|link]] <<BR>> Jordan, M. I., Ghahramani, Z. and Jaakkola, T. S., 1999  || || ||
|| Learning bounds <<BR>>  Tutorial on practical prediction theory for classification [[http://jmlr.csail.mit.edu/papers/volume6/langford05a/langford05a.pdf|link]] <<BR>> Langford, J., 2006  || || ||
|| Manifold learning <<BR>>  Laplacian eigenmaps for dimensionality reduction and data representation  <<BR>> Belkin, M. and Niyogi, P., 2003  || || ||
|| Locally Linear Embedding (LLE) <<BR>>  Nonlinear Dimensionality Reduction by Locally Linear Embedding [[http://www.sciencemag.org/content/vol290/issue5500/|link]] <<BR>> Roweis, S. T. and Saul, L. K., 2000  || || ||
|| Random forests <<BR>>  Random forests  <<BR>> Breiman, L., 2001  || || ||
|| Compressed sensing <<BR>>  Decoding by Linear Programming [[http://arxiv.org/pdf/math/0502327|link]] <<BR>> Candes,  and Tao, , 2005  Error Correction via Linear Programming [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.2255&rep=rep1&type=pdf|link]] <<BR>> Candes, , Rudelson, , Tao,  and Vershynin, , 2005  || || ||
|| Minimum description length (MDL) <<BR>>  Model Selection Using the Minimum Description Length Principle [[http://www.amstat.org/publications/tas/Bryant.htm|link]] <<BR>> Bryant, P. G. and Cordero-Brana, O. I., 2000  || || ||
|| Markov Chain Monte Carlo (MCMC) <<BR>>  An Introduction to MCMC for Machine Learning [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.13.7133&rep=rep1&type=pdf|link]] <<BR>> Andrieu, , de Freitas, , Doucet,  and Jordan, , 2003  Probabilistic Inference using Markov Chain Monte Carlo Methods [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.36.9055&rep=rep1&type=pdf|link]] <<BR>> Neal, R. M., 1993  || || ||
|| Gaussian processes <<BR>>  Gaussian Processes - A Replacement for Supervised Neural Networks? [[ftp://wol.ra.phy.cam.ac.uk/pub/mackay/gp.ps.gz|link]] <<BR>> MacKay, D. J. C., 1997  Gaussian Processes in Machine Learning [[http://dx.doi.org/10.1007/978-3-540-28650-9_4|link]] <<BR>> Rasmussen, C. E., 2003  || || ||
|| Deep belief networks <<BR>>  A Fast Learning Algorithm for Deep Belief Nets [[http://neco.mitpress.org/cgi/content/abstract/18/7/1527|link]] <<BR>> Hinton, G. E., Osindero, S. and Teh, Y. W., 2006  || || ||
|| Boosting <<BR>>  Experiments with a new boosting algorithm [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.90.4143&rep=rep1&type=pdf|link]] <<BR>> Freund, Y. and Schapire, R., 1996  Special Invited Paper. Additive Logistic Regression: A Statistical View of Boosting [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.126.7436&rep=rep1&type=pdf|link]] <<BR>> Friedman, J., Hastie, T. and Tibshirani, R., 2000  || || ||
|| Expectation Maximization (EM) <<BR>>  Maximum likelihood from incomplete data via the EM algorithm  <<BR>> Dempster, A., Laird, N. and Rubin, D., 1977  A View of the EM Algorithm that Justifies Incremental, Sparse, and other Variants [[http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.2557|link]] <<BR>> Neal, R. and Hinton, G., 1998  || || ||
|| Message passing <<BR>>  Factor Graphs and the Sum-Product Algorithm  <<BR>> Kschischang, , Frey,  and Loeliger, , 2001  || || ||
|| Model selection <<BR>>  A new look at the statistical model identification [[http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1100705|link]] <<BR>> Akaike, H., 1974  || || ||
|| Kalman filters <<BR>>  A new approach to linear filtering and prediction problems  <<BR>> Kalman, R. and others, , 1960  || || ||