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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

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

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-=== Inhalt ===
+[[https://www.isis.tu-berlin.de/course/view.php?id=5789|All information can be found in the ISIS course]]
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-In diesem Seminar wird eine Auswahl klassischer Themen aus dem Bereich des Maschinellen Lernens 
behandelt. Die Spannbreite der Themen umfasst unüberwachten Lernverfahren (Dimensionsreduktion, 
Blinde Quellentrennung, Clustering, etc.), Klassifikations- und Regressionsalgorithmen 
(SVMs, Neuronale Netze, etc.) und Methoden zur Modellselektion.
+=== Topics (tentative) ===
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-=== Voraussetzungen ===

Wir empfehlen den Besuch der Vorlesung "Maschinelles Lernen I".

=== Ablauf ===

   * Die Vorbesprechung findet am 16.11.2011 statt.
   * Die Teilnehmer wählen bis Mitte Januar ein Thema in Absprache mit dem Betreuer (siehe Themenliste). 
   * Das Seminar findet als Blockveranstaltung am Ende des Semester statt (Termin wird noch bekanntgegeben).

=== Vorträge ===

Jeder Vortrag soll 35 Minuten (+ 10 Minuten Diskussion) dauern. Der Vortrag kann wahlweise auf Deutsch oder Englisch gehalten werden. Ein guter Vortrag führt kurz in das jeweilige Thema ein, stellt die Problemstellung dar und beschreibt zusammenfassend relevante Arbeiten und Lösungen.

=== Leistungsnachweis ===

Das Seminar ist Wahlpflichtbestandteil des Master-Module "Maschinelles Lernen 1". Bachelor-Studenten können diese Master-Module auf Antrag ebenfalls belegen. Die erfolgreiche Teilnahme am Seminar ist Voraussetzung für die Modul-Prüfung.

=== Themen ===

Die Vorträge sollen jeweils 35 Minuten (+ 10 Minuten Diskussion) dauern. Wir legen Wert auf diese Zeitvorgabe und werden Vorträge bei deutlicher Überschreitung abbrechen.

|| '''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  || || ||
+|| '''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  || || ||
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+Line 19:
-|| Factor Graphs and the Sum-Product Algorithm  <<BR>> Kschischang, , Frey,  and Loeliger, , 2001  || || ||
+|| 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  || || ||
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-|| A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition  <<BR>> Rabiner, L. R., 1989  || || ||
|| Decoding by Linear Programming  <<BR>> Candes,  and Tao, , 2005  || || ||
+|| 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  || || ||
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-|| Special Invited Paper. Additive Logistic Regression: A Statistical View of Boosting  <<BR>> Friedman, J., Hastie, T. and Tibshirani, R., 2000  || || ||
|| Expectation Propagation for approximate Bayesian inference  <<BR>> Minka, T. P., 2001  || || ||
|| A new look at the statistical model identification  <<BR>> Akaike, H., 1974  || || ||
|| Error Correction via Linear Programming  <<BR>> Candes, , Rudelson, , Tao,  and Vershynin, , 2005  || || ||
+|| 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  || || ||
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-|| An Introduction to MCMC for Machine Learning  <<BR>> Andrieu, , de Freitas, , Doucet,  and Jordan, , 2003  || || ||
+|| 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  || || ||
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-|| Induction of decision trees  <<BR>> Quinlan, R., 1986  || || ||
+|| 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  || || ||
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-|| How to Use Expert Advice  <<BR>> Cesa-Bianchi, , Freund, , Haussler, , Helmbold, , Schapire,  and Warmuth, , 1997  || || ||
+|| 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  || || ||
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+Line 35:
-|| Probabilistic Inference using Markov Chain Monte Carlo Methods  <<BR>> Neal, R. M., 1993  || || ||
+|| 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  || || ||
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-|| Hierarchical Mixtures of Experts and the EM Algorithm  <<BR>> Jordan, M. I. and Jacobs, R. A., 1994  || || ||
|| An introduction to variational methods for graphical models  <<BR>> Jordan, M. I., Ghahramani, Z. and Jaakkola, T. S., 1999  || || ||
+|| 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  || || ||