大规模机器学习资源：

• 3/24/2013: RHadoop (R + Hadoop)
  http://cos.name/2013/03/rhadoop1-hadoop/
  http://cos.name/2013/03/rhadoop2-rhadoop/
• 1/22/2013: Vowpal wabbit (project to design a fast, scalable learning method by John Langford)

        Project home: http://hunch.net/~vw/
        video: http://videolectures.net/nipsworkshops2010_langford_vow/

• 1/22/2013: Large Scale Machine Learning Class from NYU (by Yann LeCun and John Langford)
  http://cilvr.cs.nyu.edu/doku.php?id=courses:bigdata:start
• 1/15/2013: PEGASUS: A Peta-scale graph mining system
  http://www.cs.cmu.edu/~pegasus/what%20is%20pegasus.htm
• 1/15/2013： Paralleled SVM
  http://code.google.com/p/psvm/
• 1/15/2013: Twister: Iterative MapReduce
  http://www.iterativemapreduce.org/

视频教学:

• 3/8/2013: Algorithms from Princeton by Dr. Robert Sedgewick
  http://algs4.cs.princeton.edu/home/
  https://class.coursera.org/algs4partI-002/lecture/index
• 2/19/2013: Machine learning from Stanford （by Dr. Andrew Ng）
  http://cs229.stanford.edu/materials.html
• 2/16/2013: Introduction to Statistics from Berkeley
  http://t.cn/zYxWfhr
• 1/31/2013: Machine Learning Course from Caltech
  http://work.caltech.edu/telecourse.html
• 1/29/2013: Algorithm Course from MIT
  http://www.youtube.com/playlist?list=PL8B24C31197EC371C
• 1/29/2013: Data Structure from Berkeley
  http://www.youtube.com/playlist?list=PLFFDF4BEE748B9159
• 1/20/2013: Machine Learning Video Lectures from CMU (by Dr. Alex Smola and Barnabas Poczos):
  http://alex.smola.org/teaching/cmu2013-10-701/intro.html

科研博客:

• 1/22/2013: Blog created by John Langford, research scientist from Microsoft Research
  http://hunch.net
  

其他资源 (E-英语; C-中文):

• (E) 2/19/2013: Deep Learning
  http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial
  
• (E) 2/16/2013: Unix AWK Script
  http://www.grymoire.com/Unix/Awk.html
  
• (E) 1/22/2013: Stochastic Gradient Descent （Code and Data for SGD performs on SVM & CRF）
  http://leon.bottou.org/projects/sgd
  
• (E) 1/22/2013: Online Learning
  http://bigeye.au.tsinghua.edu.cn/DragonStar2012/docs/online.pdf
  
• (E) 1/21/2013: Linear Discriminant Analysis(LDA) & Principal Component Analysis(PCA) :
  http://www.cvip.uofl.edu/wwwcvip/education/ECE523/LDA%20Tutorial.pdf
• (C)2/16/2013: Unix AWK Script
  http://coolshell.cn/articles/9070.html
  
• (C)1/28/2013: Regression and variance
  http://www.cnblogs.com/LeftNotEasy/archive/2010/12/19/mathmatic_in_machine_learning_2_regression_and_bias_variance_trade_off.html
  
• (C) 1/28/2013: Regression and Gradient Descent
  http://www.cnblogs.com/LeftNotEasy/archive/2010/12/05/mathmatic_in_machine_learning_1_regression_and_gradient_descent.html
  
• (C) 1/28/2013: Boosting and Gradient Descent
  http://www.cnblogs.com/LeftNotEasy/archive/2011/01/02/machine-learning-boosting-and-gradient-boosting.html
  
• (C) 1/22/2013: EM Algorithm:
  http://www.cnblogs.com/jerrylead/archive/2011/04/06/2006936.html
• (C) 1/21/2013: Linear Discriminant Analysis(LDA) & Principal Component Analysis(PCA) : http://www.cnblogs.com/LeftNotEasy/archive/2011/01/08/lda-and-pca-machine-learning.html
• (C) 1/21/2013: Singular Value Decomposition(SVD):
  http://www.cnblogs.com/LeftNotEasy/archive/2011/01/19/svd-and-applications.html

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