牛们的blog （人工智能与机器学习）
国外人工智能界牛人主页
以前转过一个计算机视觉领域内的牛人简介，现在转一个更宽范围内的牛人简介：
http://people.cs.uchicago.edu/~niyogi/
http://www.cs.uchicago.edu/people/
http://pages.cs.wisc.edu/~jerryzhu/
http://www.kyb.tuebingen.mpg.de/~chapelle
http://people.cs.uchicago.edu/~xiaofei/
http://www.cs.uiuc.edu/homes/dengcai2/
http://www.kyb.mpg.de/~bs
http://research.microsoft.com/~denzho/
http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item5 (resources for the book of the introduction of data mining by Pang-ning Tan et.al. )（国内已经有相应的中文版）
http://www.cs.toronto.edu/~roweis/lle/publications.html (lle算法源代码及其相关论文)
http://dataclustering.cse.msu.edu/index.html#software（data clustering）
http://www.cs.toronto.edu/~roweis/ (里面有好多资源)
http://www.cse.msu.edu/~lawhiu/ (manifold learning)
http://www.math.umn.edu/~wittman/mani/ (manifold learning demo in matlab)
http://www.iipl.fudan.edu.cn/~zhangjp/literatures/MLF/INDEX.HTM (manifold learning in matlab)
http://videolectures.net/mlss05us_belkin_sslmm/ (semi supervised learning with manifold method by Belkin)
http://isomap.stanford.edu/ (isomap主页)
http://web.mit.edu/cocosci/josh.html MIT TENENBAUM J B主页
http://web.engr.oregonstate.edu/~tgd/ （国际著名的人工智能专家 Thomas G. Dietterich）
http://www.cs.berkeley.edu/~jordan/ （MIchael I.Jordan）
http://www.cs.cmu.edu/~awm/ (Andrew W. Moore's homepage)
http://learning.cs.toronto.edu/ （加拿大多伦多大学机器学习小组）
http://www.cs.cmu.edu/~tom/ （Tom Mitchell，里面有与教材匹配的slide。）
Kernel Methods
Alexander J. Smola
Maximum Mean Discrepancy (MMD), Hilbert-Schmidt Independence Criterion (HSIC)
Bernhard Schölkopf
Kernel PCA
James T Kwok
Pre-Image, Kernel Learning, Core Vector Machine(CVM)
Jieping Ye
Kernel Learning, Linear Discriminate Analysis, Dimension Deduction
Multi-Task Learning
Andreas Argyriou
Multi-Task Feature Learning
Charles A. Micchelli
Multi-Task Feature Learning, Multi-Task Kernel Learning
Massimiliano Pontil
Multi-Task Feature Learning
Yiming Ying
Multi-Task Feature Learning, Multi-Task Kernel Learning
Semi-supervised Learning
Partha Niyogi
Manifold Regularization, Laplacian Eigenmaps
Mikhail Belkin
Manifold Regularization, Laplacian Eigenmaps
Vikas Sindhwani
Manifold Regularization
Xiaojin Zhu
Graph-based Semi-supervised Learning
Multiple Instance Learning
Sally A Goldman
EM-DD, DD-SVM, Multiple Instance Semi Supervised Learning(MISS)
Dimensionality Reduction
Neil Lawrence
Gaussian Process Latent Variable Models (GPLVM)
Lawrence K. Saul
Maximum Variance Unfolding(MVU), Semidefinite Embedding(SDE)
Machine Learning
Michael I. Jordan
Graphical Models
John Lafferty
Diffusion Kernels, Graphical Models
Daphne Koller
Logic, Probability
Zhang Tong
Theoretical Analysis of Statistical Algorithms, Multi-task Learning, Graph-based Semi-supervised Learning
Zoubin Ghahramani
Bayesian approaches to machine learning
Machine Learning @ Toronto
Statitiscal Machine Learning & Optimization
Jerome H Friedman
GLasso, Statistical view of AdaBoost, Greedy Function Approximation
Thevor Hastie
Lasso
Stephen Boyd
Convex Optimization
C.J Lin
Libsvm
http://www.dice.ucl.ac.be/mlg/
半监督流形学习（流形正则化）
http://manifold.cs.uchicago.edu/
模式识别和神经网络工具箱
http://www.ncrg.aston.ac.uk/netlab/index.php
机器学习开源代码
http://mloss.org/software/tags/large-scale-learning/
统计学开源代码
http://www.wessa.net/
matlab各种工具箱链接
http://www.tech.plym.ac.uk/spmc/links/matlab/matlab_toolbox.html
统计学学习经典在线教材
http://www.statistics4u.info/
机器学习开源源代码
http://mloss.org/software/language/matlab/
国外人工智能界牛人主页
以前转过一个计算机视觉领域内的牛人简介，现在转一个更宽范围内的牛人简介：
http://people.cs.uchicago.edu/~niyogi/
http://www.cs.uchicago.edu/people/
http://pages.cs.wisc.edu/~jerryzhu/
http://www.kyb.tuebingen.mpg.de/~chapelle
http://people.cs.uchicago.edu/~xiaofei/
http://www.cs.uiuc.edu/homes/dengcai2/
http://www.kyb.mpg.de/~bs
http://research.microsoft.com/~denzho/
http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item5 (resources for the book of the introduction of data mining by Pang-ning Tan et.al. )（国内已经有相应的中文版）
http://www.cs.toronto.edu/~roweis/lle/publications.html (lle算法源代码及其相关论文)
http://dataclustering.cse.msu.edu/index.html#software（data clustering）
http://www.cs.toronto.edu/~roweis/ (里面有好多资源)
http://www.cse.msu.edu/~lawhiu/ (manifold learning)
http://www.math.umn.edu/~wittman/mani/ (manifold learning demo in matlab)
http://www.iipl.fudan.edu.cn/~zhangjp/literatures/MLF/INDEX.HTM (manifold learning in matlab)
http://videolectures.net/mlss05us_belkin_sslmm/ (semi supervised learning with manifold method by Belkin)
http://isomap.stanford.edu/ (isomap主页)
http://web.mit.edu/cocosci/josh.html MIT TENENBAUM J B主页
http://web.engr.oregonstate.edu/~tgd/ （国际著名的人工智能专家 Thomas G. Dietterich）
http://www.cs.berkeley.edu/~jordan/ （MIchael I.Jordan）
http://www.cs.cmu.edu/~awm/ (Andrew W. Moore's homepage)
http://learning.cs.toronto.edu/ （加拿大多伦多大学机器学习小组）
http://www.cs.cmu.edu/~tom/ （Tom Mitchell，里面有与教材匹配的slide。）
Kernel Methods
Alexander J. Smola
Maximum Mean Discrepancy (MMD), Hilbert-Schmidt Independence Criterion (HSIC)
Bernhard Schölkopf
Kernel PCA
James T Kwok
Pre-Image, Kernel Learning, Core Vector Machine(CVM)
Jieping Ye
Kernel Learning, Linear Discriminate Analysis, Dimension Deduction
Multi-Task Learning
Andreas Argyriou
Multi-Task Feature Learning
Charles A. Micchelli
Multi-Task Feature Learning, Multi-Task Kernel Learning
Massimiliano Pontil
Multi-Task Feature Learning
Yiming Ying
Multi-Task Feature Learning, Multi-Task Kernel Learning
Semi-supervised Learning
Partha Niyogi
Manifold Regularization, Laplacian Eigenmaps
Mikhail Belkin
Manifold Regularization, Laplacian Eigenmaps
Vikas Sindhwani
Manifold Regularization
Xiaojin Zhu
Graph-based Semi-supervised Learning
Multiple Instance Learning
Sally A Goldman
EM-DD, DD-SVM, Multiple Instance Semi Supervised Learning(MISS)
Dimensionality Reduction
Neil Lawrence
Gaussian Process Latent Variable Models (GPLVM)
Lawrence K. Saul
Maximum Variance Unfolding(MVU), Semidefinite Embedding(SDE)
Machine Learning
Michael I. Jordan
Graphical Models
John Lafferty
Diffusion Kernels, Graphical Models
Daphne Koller
Logic, Probability
Zhang Tong
Theoretical Analysis of Statistical Algorithms, Multi-task Learning, Graph-based Semi-supervised Learning
Zoubin Ghahramani
Bayesian approaches to machine learning
Machine Learning @ Toronto
Statitiscal Machine Learning & Optimization
Jerome H Friedman
GLasso, Statistical view of AdaBoost, Greedy Function Approximation
Thevor Hastie
Lasso
Stephen Boyd
Convex Optimization
C.J Lin
Libsvm
http://www.dice.ucl.ac.be/mlg/
半监督流形学习（流形正则化）
http://manifold.cs.uchicago.edu/
模式识别和神经网络工具箱
http://www.ncrg.aston.ac.uk/netlab/index.php
机器学习开源代码
http://mloss.org/software/tags/large-scale-learning/
统计学开源代码
http://www.wessa.net/
matlab各种工具箱链接
http://www.tech.plym.ac.uk/spmc/links/matlab/matlab_toolbox.html
统计学学习经典在线教材
http://www.statistics4u.info/
机器学习开源源代码
http://mloss.org/software/language/matlab/
