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=== Topics (tentative) ===

|| '''Paper(s)''' || '''Betreuer''' || '''Vortragender''' ||
|| Nonlinear Dimensionality Reduction by Locally Linear Embedding [[http://www.sciencemag.org/content/vol290/issue5500/|link]] <<BR>> Roweis, S. T. and Saul, L. K., 2000 || || ||
|| 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 || || ||
|| Factor Graphs and the Sum-Product Algorithm [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.54.1570&rep=rep1&type=pdf|link]] <<BR>> Kschischang, , Frey, and Loeliger, , 2001 || || ||
|| Gaussian Processes in Machine Learning [[http://dx.doi.org/10.1007/978-3-540-28650-9_4|link]] <<BR>> Rasmussen, C. E., 2003 || || ||
|| 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 || || ||
|| Decoding by Linear Programming [[http://arxiv.org/pdf/math/0502327|link]] <<BR>> Candes, and Tao, , 2005 || || ||
|| Self-organizing formation of topologically correct feature maps <<BR>> Kohonen, T., 1982 || || ||
|| 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 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 || || ||
|| A new look at the statistical model identification [[http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1100705|link]] <<BR>> Akaike, H., 1974 || || ||
|| 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 || || ||
|| A Global Geometric Framework for Nonlinear Dimensionality Reduction [[http://isomap.stanford.edu/|link]] <<BR>> Tenenbaum, J. B., de Silva, V. and Langford, J. C., 2000 || || ||
|| 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 || || ||
|| Perspectives on Sparse Bayesian Learning [[http://books.nips.cc/papers/files/nips16/NIPS2003_AA32.pdf|link]] <<BR>> Wipf, D. P., Palmer, J. A. and Rao, B. D., 2003 || || ||
|| 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 || || ||
|| 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 || || ||
|| How to Use Expert Advice [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.86.7476&rep=rep1&type=pdf|link]] <<BR>> Cesa-Bianchi, , Freund, , Haussler, , Helmbold, , Schapire, and Warmuth, , 1997 || || ||
|| 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 || || ||
|| 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 || || ||
|| 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 || || ||
|| 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 || || ||
|| Gaussian Processes in Reinforcement Learning [[http://books.nips.cc/papers/files/nips16/NIPS2003_CN01.pdf|link]] <<BR>> Rasmussen, C. E. and Kuss, M., 2003 || || ||
|| 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 || || ||


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

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

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