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.)

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

Ensemble learning
Induction of decision trees. Quinlan, R., 1986 link Hierarchical Mixtures of Experts and the EM Algorithm. Jordan, M. I. and Jacobs, R. A., 1994 link

Spectral clustering
A tutorial on spectral clustering. Von Luxburg, U., 2007 link

Expectation propagation
Expectation Propagation for approximate Bayesian inference. Minka, T. P., 2001 link

Hidden Markov Models (HMM)
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Rabiner, L. R., 1989 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
An introduction to variational methods for graphical models. Jordan, M. I., Ghahramani, Z. and Jaakkola, T. S., 1999 link

Learning bounds
Tutorial on practical prediction theory for classification. Langford, J., 2006 link

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. Roweis, S. T. and Saul, L. K., 2000 link

Random forests
Random forests. Breiman, L., 2001

Compressed sensing
Decoding by Linear Programming. Candes, and Tao, , 2005 link Error Correction via Linear Programming. Candes, , Rudelson, , Tao, and Vershynin, , 2005 link

Minimum description length (MDL)
Model Selection Using the Minimum Description Length Principle. Bryant, P. G. and Cordero-Brana, O. I., 2000 link

Markov Chain Monte Carlo (MCMC)
An Introduction to MCMC for Machine Learning. Andrieu, , de Freitas, , Doucet, and Jordan, , 2003 link Probabilistic Inference using Markov Chain Monte Carlo Methods. Neal, R. M., 1993 link

Gaussian processes
Gaussian Processes - A Replacement for Supervised Neural Networks?. MacKay, D. J. C., 1997 link Gaussian Processes in Machine Learning. Rasmussen, C. E., 2003 link

Deep belief networks
A Fast Learning Algorithm for Deep Belief Nets. Hinton, G. E., Osindero, S. and Teh, Y. W., 2006 link

Boosting
Experiments with a new boosting algorithm. Freund, Y. and Schapire, R., 1996 link Special Invited Paper. Additive Logistic Regression: A Statistical View of Boosting. Friedman, J., Hastie, T. and Tibshirani, R., 2000 link

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. Neal, R. and Hinton, G., 1998 link

Message passing
Factor Graphs and the Sum-Product Algorithm. Kschischang, , Frey, and Loeliger, , 2001

Model selection
A new look at the statistical model identification. Akaike, H., 1974 link

Kalman filters
A new approach to linear filtering and prediction problems. Kalman, R. and others, , 1960