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Machine Learning reading groups

PS: From Yaroslav Bulatov’s Blog Post

If you are looking for ideas for things to read, you should check out what other Machine Learning Reading Groups are reading. I compiled the following list by searching google/asking people.

This list is not very up to date. This is a permalink so feel free to link to it. Please let me know through email, or comment section about other up-to-date MLRGs you know (site must list papers)

Current MLRGs
(as of Feb 19th 2005)

  • Oregon State U (Roweis, Tishby, Jordan, Collins, Caticha)– mlrg
  • Gatsby (McKay, ARD, LDA) — mlrg
  • Humboldt-Universitat zu Berlin (ICML, KDD papers)– mlrg
  • TTIC Chicago list of readings (Langford, Kakade, McAllester)seminars
  • University of New Mexico (Jaakkola, Mooney, Scholkopf) — mlrg
  • Oregon Health and Science U. (Seeger, Geman, MacKay) — mlrg
  • Washington University (Weiss, Srebro) — mlrg
  • MIT (Neal, Hofmann, Tishby) — mlrg
  • University of California, Irvine (Blei, Jordan, Welling) — mlrg
  • University of Amsterdam (Manning, Koller, Collins, Valiant) mlrg
  • Hebrew University (IB, SVM, Rademacher, Chechnik) mlrg
  • Cornell (Jordan, Schuurmans,Cristianini) — mlrg
  • South Carolina (Kakade, Dresner, Feigenbaum) mlrg
  • U. Alberta (Sutton, RL, MaxEnt)– mlrg
  • Weizemann Institute, (vision oriented, Jordan, Buntine) — mlrg
  • Helsinki U of Tech (mostly neuroscience, Sejnowski) — mlrg
  • University of London (vision oriented) — mlrg
  • Rutgers (Michael Jordan‘s notes) — mlrg
  • UMass (vision oriented, ICA, kernel PCA) — mlrg
  • Edinburgh (NLP group, Hoffman, Manning,Collins) — mlrg
  • U. of California, San Diego (Tishby, Lafferty, Joachims, Herbrich) — mlrg
  • UTexas (Aggarwal, Altun, Manning) — mlrg

Seminars,Journal Clubs,slightly outdated MLRGs

  • Columbia (November) — mlrg
  • U.Toronto (July)– mlrg
  • Tufts University (December) — mlrg
  • Australian National U. (December)– mlrg
  • Centre Universitaire d’Informatique (June) —journal club
  • Microsoft Research (Herbrich, large-margin, factor graphs, kernels) — mlrg
  • CWI Journal Club (Netherlands) journal club
  • Seminars at NYU (Langford, Saul) seminars
  • Graphical Models Reading Group at CMU (about 10 months old) mlrg

Who is being read?
Below is the list of most popular authors ranked by how many groups in the “current MLRGs” section had them listed:

9 – Koller
8 – Jordan
7 – Weiss
6 – Taskar, Saul, McCallum, Lafferty, Ghahramani, Friedman, Collins
5 – Roweis, Pereira, Ng, Domingos
4 – Zhu, Tishby, Russell, Roth, Li, Kearns, Jaakkola, Guestrin, Blei
3 – Zhou, Yair, Wong, Wellner, Tenenbaum, Slonim, Singh, Singer, Scholkopf, Schapire, Mooney, Mansour, Manning, MacKay, Joachims, Hofmann, Griffiths, Freund, Freeman, Dengyong, Breiman, Bartlett, Altun

Questions:

  • What other reading groups have websites?
  • What are other ways of finding interesting papers?
There is another one from its comment:
Machine Learning Lunchtime Chats at CMU:
http://www-2.cs.cmu.edu/~learning/

http://www.eecs.berkeley.edu/~wainwrig/

(this is a very nice homepage of a professor on machine learning)

http://www1.cs.columbia.edu/~jebara/index.html

(another homepage of a professor on machine learning)

http://www.athenasc.com/convexity.html

http://www.athenasc.com/nonlinbook.html

http://www.eecs.berkeley.edu/~polak/EE227B.d/EE227B.html

http://measuringmeasures.blogspot.com/

http://measuringmeasures.com/

http://pindancing.blogspot.com/

http://blog.smellthedata.com/

http://ecome.wordpress.com/

ps: the following list is the useful articles on machine learning

http://measuringmeasures.com/blog/2010/3/12/learning-about-machine-learning-2nd-ed.html

http://pindancing.blogspot.com/2010/01/learning-about-machine-learniing.html

http://news.ycombinator.com/item?id=1055042

http://blog.smellthedata.com/2010/07/choosing-first-machine-learning-project.html

ps: the following article gives a list of machine learing blogs

http://ecome.wordpress.com/2010/11/15/machines-learning-blogs/

ps: the following link is the program of statistical machine learning at Berkeley University

http://www.stat.berkeley.edu/~gvrocha/statlearning/index.html

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