100 Most Popular Machine Learning Video Talks

  1. 26971 views, 1:00:45,  Gaussian Process Basics, David MacKay, 8 comments
  2. 7799 views, 3:08:32, Introduction to Machine Learning, Iain Murray
  3. 16092 views, 1:28:05, Introduction to Support Vector Machines, Colin Campbell, 22 comments
  4. 5755 views, 2:53:54, Probability and Mathematical Needs, Sandrine Anthoine, 2 comments
  5. 7960 views, 3:06:47, A tutorial on Deep Learning, Geoffrey E. Hinto
  6. 3858 views, 2:45:25, Introduction to Machine Learning, John Quinn, 1 comment
  7. 13758 views, 5:40:10, Statistical Learning TheoryJohn Shawe-Taylor3 comments
  8. 12226 views, 1:01:20, Semisupervised Learning Approaches, Tom Mitchell,8 comments
  9. 1596 views, 1:04:23, Why Bayesian nonparametrics?Zoubin Ghahramani,  1 comment
  10. 11390 views, 3:52:22, Markov Chain Monte Carlo Methods, Christian P. Robert,5 comments
  11. 3153 views, 2:15:00, Data mining and Machine learning algorithms, José L. Balcázar, 1 comment
  12. 10322 views, 5:15:43, Graphical models, Zoubin Ghahramani, 23 comments
  13. 11071 views, 1:05:40, Dirichlet Processes, Chinese Restaurant Processes, and all that,Michael I. Jordan7 comments
  14. 10550 views, 1:06:55, Generative Models for Visual Objects and Object Recognition via Bayesian Inference, Fei-Fei Li, 11 comments
  15. 9312 views, 03:21, K-nearest neighbor classification, Antal van den Bosch,7 comments
  16. 4800 views, 2:07:31, Patterns in Vector Spaces, Elisa Ricci, 1 comment
  17. 736 views, 16:55, Twitter Sentiment  in Financial Domain, Miha Grčar, 1 comment
  18. 6789 views, 2:06:40, Introduction to kernel methods, Bernhard Schölkopf,  5 comments
  19. 6849 views, 2:54:37, Some Mathematical Tools for Machine Learning, Chris Burges, 6 comments
  20. 6792 views, 1:24:46, Bayesian Learning, Zoubin Ghahramani,  9 comments
  21. 6689 views, 4:33:48, Graphical Models and Variational Methods, Christopher Bishop, 11 comments
  22. 844 views, 17:05, High-Dimensional Graphical Model Selection, Animashree Anandkumar
  23. 5862 views, 57:16, Introduction to feature selection, Isabelle Guyon,  1 comment
  24. 5541 views, 2:14:21, Introduction to kernel methods, Alexander J. Smola,  8 comments
  25. 2304 views, 3:22:46, Introduction to Kernel Methods, Liva Ralaivola,  1 comment 
  26. 723 views, 16:26, Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries, Zhen James Xiang
  27. 1628 views, 23:12, Gradient Boosted Decision Trees on Hadoop, Jerry Ye
  28. 5169 views, 4:16:53, Learning with Kernels,4 comments
  29. 2038 views, 03:18, Scikitlearn, Gael Varoquaux
  30. 4965 views, 32:36, The Dynamics of AdaBoost, Cynthia Rudin,  3 comments
  31. 4433 views, 2:16:17, Sequential Monte Carlo methods, Arnaud Doucet, 9 comments
  32. 4859 views, 1:37:46, Online Learning and Game Theory, Adam Kalai,  3 comments
  33. 4237 views, 20:36, Learning to align: a statistical approach, Elisa Ricci, 1 comment 
  34. 2645 views, 21:49, Online Dictionary Learning for Sparse Coding, Julien Mairal, 1 comment
  35. 4727 views, 3:13:52, Bayesian Inference: Principles and Practice, Mike Tipping, 6 comments
  36. 1419 views, 2:49:30, Online Learning, Peter L. Bartlett
  37. 2973 views, 21:01, Training a Binary Classifier with the Quantum Adiabatic Algorithm, Hartmut Neven, 1 comment 
  38. 3973 views, 08:55, Machine Learning for Stock Selection, Charles X. Ling,3 comments
  39. 3900 views, 2:56:35, Machine learning and finance, László Györfi, 3 comments
  40. 3517 views, 2:10:19, Learning with Gaussian Processes, Carl Edward Rasmussen,7 comments
  41. 222 views, 29:03, Generating Possible Interpretations for Statistics from Linked Open Data,Heiko Paulheim
  42. 4089 views, 2:32:26, Graph Matching Algorithms, Terry Caelli,  6 comments
  43. 3948 views, 3:39:05, Clustering – An overview, Marina Meila,  1 comment
  44. 3903 views, 2:11:59, An Introduction to Pattern Classification, Elad Yom Tov,1 comment
  45. 3896 views, 5:18:05, Statistical Learning Theory, Olivier Bousquet, 3 comments 
  46. 1541 views, 38:10, Learning with similarity functions, Maria Balcan
  47. 51 views, 1:00:30, A Flexible Model for Count Data: The COM-Poisson Distribution, Galit Shmuél
  48. 331 views, 41:53, Automatic Discovery of Patterns in News Content, Nello Cristianini,2 comments
  49. 1132 views, 2:31:35, Gaussian Processes, Edwin V. Bonilla
  50. 2256 views, 1:08:39, Lecture 1 – The Motivation & Applications of Machine Learning, Andrew Ng
  51. 666 views, 21:47, On the Usefulness of Similarity based Projection Spaces for Transfer Learning, Emilie Morvant
  52. 1112 views, 36:35, Robust PCA and Collaborative Filtering: Rejecting Outliers, Identifying Manipulators, Constantine Caramanis
  53. 3294 views, 2:01:49, The EM algorithm and Mixtures of Gaussians, Joaquin Quiñonero Candela, 4 comments
  54. 3444 views, 5:35:17, Independent Component Analysis, Jean-François Cardoso, 2 comments
  55. 1918 views, 19:47, Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations, Honglak Lee
  56. 790 views, 1:00:20, Classification and Clustering in Large Complex Networks, Ina Eliasi-Rad
  57. 986 views, 2:44:35, Restricted Boltzmann Machines and Deep Belief Nets, Marcus Frean
  58. 23 views, 17:29, Improved Initialisation and Gaussian Mixture Pairwise Terms for Dense Random Fields with Mean-field Inference, Vibhav Vineet
  59. 1915 views, 1:22:16, Lecture 11 – Bayesian Statistics and Regularization, Andrew Ng
  60. 3129 views, 4:31:39, Kernel Methods, Alexander J. Smola 2 comments
  61. 2577 views, 1:21:29, Graphical models, Zoubin Ghahramani
  62. 2160 views, 1:00:37, Should all Machine Learning be Bayesian? Should all Bayesian models be non-parametric?, Zoubin Ghahramani,  2 comments
  63. 3018 views, 4:35:51, Graphical Models, Variational Methods, and Message-Passing, Martin J. Wainwright, 6 comments
  64. 3017 views, 3:43:43, Introduction to Kernel Methods, Bernhard Schölkopf, 1 comment
  65. 1257 views, 1:24:39, Reinforcement learning: Tutorial + Rethinking State, Action & Reward, Satinder Singh
  66. 1044 views, 18:34, On the stability and interpretability of prognosis signatures in breast cancer, Anne-Claire Haury,1 comment 
  67. 2827 views, 00:58, Artificial intelligence: An instance of Aibo ingenuity, Michael Littman,2 comments
  68. 163 views, 22:35, Exploiting Information Extraction, Reasoning and Machine Learning for Relation Prediction, Xueyan Jiang,2 comments
  69. 1704 views, 2:42:22, Theory and Applications of Boosting, Robert Schapire,1 comment
  70. 387 views, 18:48, High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity, Po-Ling Loh
  71. 1912 views, 38:30, Machine learning and kernel methods for computer vision, Francis R. Bach
  72. 2755 views, 32:18, Neighbourhood Components Analysis, Sam Roweis,1 comment
  73. 2295 views, 28:18, Learning an Outlier-Robust Kalman Filter, Jo-Anne Ting,1 comment
  74. 1308 views, 25:08, Probabilistic Machine Learning in Computational Advertising, Thore Graepel
  75. 2670 views, 4:22:31, Gaussian Processes, Carl Edward Rasmussen, 2 comments
  76. 1772 views, 58:42, Probabilistic Decision-Making Under Model Uncertainty,  Joelle Pineau
  77. 2198 views, 58:51, Who is Afraid of Non-Convex Loss Functions?,  Yann LeCun
  78. 339 views, 54:15, Machine Learning Markets, Amos Storkey
  79. 2560 views, 1:49:01, Generalized Principal Component Analysis (GPCA), Rene Vidal,8 comments
  80. 1247 views, 25:00, FPGA-based MapReduce Framework for Machine Learning, Ningyi Xu
  81. 2527 views, 58:39, Latent Semantic Variable Models, Thomas Hofmann,3 comments
  82. 324 views, 18:31, k-NN Regression Adapts to Local Intrinsic Dimension, Samory Kpotufe
  83. 1485 views, 1:20:37, Lecture 14 – The Factor Analysis Model, Andrew Ng
  84. 2000 views, 1:11:49, Hierarchical Clustering, Yee Whye Teh
  85. 316 views, 16:38, Discussion of Erik Sudderth’s talk: NPB Hype or Hope?, Yann LeCun
  86. 309 views, 16:15, A Collaborative Mechanism for Crowdsourcing Prediction Problems, Jacob Aberneth
  87. 1993 views, 39:15, Speeding Up Stochastic Gradient Descent, Yoshua Bengio
  88. 126 views, 24:42, LODifier: Generating Linked Data from Unstructured Text, Isabelle Augenstein
  89. 304 views, 19:47, Iterative Learning for Reliable Crowdsourcing Systems, Sewoong Oh
  90. 1246 views, 24:03, Collaborative Filtering with Temporal Dynamics, Yehuda Koren
  91. 714 views, 21:56, HIV-Haplotype Inference using a Constraintbased Dirichlet Process Mixture Model, Sandhya Prabhakaran, Melanie Rey
  92. 1272 views, 22:40, Modeling the S&P 500 Index using the Kalman Filter and the LagLasso, Nicolas Mahle
  93. 2064 views, 10:47, Ten problems for the next 10 years, Pedro Domingos, 1 comment
  94. 2097 views, 23:15Best Paper – Information-Theoretic Metric Learning, Brian Kulis
  95. 926 views, 1:10:31, Neuroscience, cognitive science and machine learning, Konrad Körding
  96. 2210 views, 1:21:57, Introduction to Kernel Methods, Partha Niyogi, 5 comments
  97. 291 views, 12:00, Fast and Accurate k-means For Large Datasets, Michael Shindler
  98. 2203 views, 2:56:16,Probabilistic and Bayesian Modelling I, Manfred Opper, 1 comment
  99. 2198 views, 1:00:00, Nonparametric Bayesian Models in Machine Learning, Zoubin Ghahramani
  100. 1901 views, 48:34,Machine Learning for Intrusion Detection, Pavel Laskov

Also: Stop by UCI (UC Irving) Machine Learning Repository for 295 Data Sets that can be accessed via searchable interface.  Other Related articles

Related articles

100 Most Popular Machine Learning Video Talks的更多相关文章

  1. How do I learn machine learning?

    https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644   How Can I Learn X? ...

  2. A Gentle Guide to Machine Learning

    A Gentle Guide to Machine Learning Machine Learning is a subfield within Artificial Intelligence tha ...

  3. Machine Learning and Data Mining(机器学习与数据挖掘)

    Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcemen ...

  4. [C2P3] Andrew Ng - Machine Learning

    ##Advice for Applying Machine Learning Applying machine learning in practice is not always straightf ...

  5. Machine Learning Note Phase 1( Done!)

    Machine Learning 这是第一份机器学习笔记,创建于2019年7月26日,完成于2019年8月2日. 该笔记包括如下部分: 引言(Introduction) 单变量线性回归(Linear ...

  6. Machine Learning Trick of the Day (1): Replica Trick

    Machine Learning Trick of the Day (1): Replica Trick 'Tricks' of all sorts are used throughout machi ...

  7. 机器学习算法之旅A Tour of Machine Learning Algorithms

    In this post we take a tour of the most popular machine learning algorithms. It is useful to tour th ...

  8. My Reading List - Machine Learning && Computer Vision

    本博客汇总了个人在学习过程中所看过的一些论文.代码.资料以及常用的资源与网站,为了便于记录自身的学习过程,将其整理于博客之中. Machine Learning (1) Machine Learnin ...

  9. Note for video Machine Learning and Data Mining——Linear Model

    Here is the note for lecture three. the linear model Linear model is a basic and important model in ...

随机推荐

  1. ns3 模拟无线网络通信

    /* -*- Mode:C++; c-file-style:"gnu"; indent-tabs-mode:nil; -*- */ /* * This program is fre ...

  2. 团队作业7——第二次项目冲刺(Beta版本12.04——12.07)

    1.当天站立式会议照片 本次会议在5号公寓3楼召开,本次会议内容:①:熟悉每个人想做的模块.②:根据项目要求还没做的完成. 2.每个人的工作 经过会议讨论后确定了每个人的分工 组员 任务 陈福鹏 实现 ...

  3. 对于Redis的了解

    Redis :高性能的key-value数据库,支持存储的value类型包括字符串.链表.集合.有序集合.哈希类型. redis使用两种文件格式:全量数据和增量请求. 全量数据格式是将内存中的数据写入 ...

  4. js 杂项(一)函数篇

    你还应该知道箭头函数( => )可以用来保留上下文.这个方法也可以:

  5. cxGrid 单元格回车移到下一行,当移到最后一个单元格时回车新增一行【转】

    1 在TcxGridDBTableView中,设定属性 NewItemRow.Visible = True 2 在cxgrid中输入数据怎样回车换行  在TcxGridDBTableView中  将属 ...

  6. Powershell笔记之MVA课程

    很早之前看过MVA的Powershell课程,最近准备回顾一下,还是有一些意外的收获. <<快速入门 : PowerShell 3.0 高级工具和脚本>> 1. Invoke- ...

  7. jsp中的下载链接

    1.下载链接jsp界面(a链接直接链文件可以看出文件在服务器中的路径,用servlet处理的链接则看不出) <%@ page language="java" contentT ...

  8. BZOJ 3498 PA2009 Cakes

    本题BZOJ权限题,但在bzojch上可以看题面. 题意: N个点m条无向边,每个点有一个点权a. 对于任意一个三元环(i,j,k)(i<j<k),它的贡献为max(ai,aj,ak) 求 ...

  9. java的finally用法

    finally作为异常处理的一部分,它只能用在try/catch语句中,并且附带一个语句块,表示这段语句最终一定会被执行(不管有没有抛出异常),经常被用在需要释放资源的情况下. 之前在写爬虫的时候数据 ...

  10. [BZOJ3295][Cqoi2011]动态逆序对 CDQ分治&树套树

    3295: [Cqoi2011]动态逆序对 Time Limit: 10 Sec  Memory Limit: 128 MB Description 对于序列A,它的逆序对数定义为满足i<j,且 ...