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/