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人工智能与机器学习blog资源

发布时间:2017-08-10编辑:adwind查看次数:

    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/

关键字词:人工智能、机器学习、学习资料