videolectures 100個最受歡迎的機器學習視頻

轉自：http://blog.videolectures.net/100-most-popular-machine-learning-talks-at-videolectures-net/

100-most-popular-machine-learning-talks-at-videolectures-net/

 

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 Theory, John Shawe-Taylor, 3 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. Jordan, 7 comments

    Daniel Wolpert – we are bayesian inference machines – 1 (Photo credit: pseudonomad)

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

    Cover of Statistical Learning Theory

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

    Zoubin Ghahramani – ‘Internet search queries’ (Photo credit: Engineering at Cambridge)

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, 23:15, Best 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

 

• Scalable Machine Learning Course (datascience101.wordpress.com)
• 7th Annual Machine Learning Symposium (hunch.net)
• Message-Oriented Object Design and Machine Learning in JavaScript (architects.dzone.com)
• Large Scale (Machine) Learning at Twitter (videolectures.net)
• Hidden Benefits of Online Learning (i-programmer.info)
• Model checking and model understanding in machine learning (andrewgelman.com)
• Machine Learning Book for Students and Researchers (googleresearch.blogspot.com)
• Wagstaff (2012, jpl) machine learning that matters (slideshare.net)
• Why becoming a data scientist might be easier than you think (gigaom.com)
• The Man-Machine Framework: How to Build Machine-Learning Applications the Right Way(forbes.com)

 

Tagged: artificial intelligence, Bayesian inference, Computer Science, John Shawe-Taylor, Kernel Methods, machine learning, Michael I. Jordan, Support vector machine, Technology, Zoubin Ghahraman