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23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2009
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Deep Learning in Natural Language Processing by Ronan Collobert, Jason Weston, 2010 (rec 2009)
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Deep Learning with Multiplicative Interactions by Geoffrey E. Hinton, 2010 (rec 2009)
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Learning and Inference in Low-Level Vision by Yair Weiss, 2010 (rec 2009)
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Making Very Large-Scale Linear Algebraic Computations Possible Via Randomization by Gunnar Martinsson, 2010 (rec 2009)
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Model-Based Reinforcement Learning by Michael Littman, 2010 (rec 2009)
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Relative Entropy by Sergio Verdu, 2010 (rec 2009)
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Sequential Monte-Carlo Methods by Arnaud Doucet, Nando de Freitas, 2010 (rec 2009)
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Sparse Methods for Machine Learning: Theory and Algorithms by Francis R. Bach, 2010 (rec 2009)
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The Rat Vibrissal Array as a Model Sensorimotor System by Mitra Hartmann, 2010 (rec 2009)
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24th Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2010
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Optimization Algorithms in Machine Learning by Stephen J. Wright, 2011 (rec 2010)
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Reinforcement Learning in Humans and Other Animals by Nathaniel Daw, 2011 (rec 2010)
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24th Annual International Conference on Machine Learning (ICML), Corvallis 2007
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Best Paper - Information-Theoretic Metric Learning by Brian Kulis, 2007
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26th International Conference on Machine Learning (ICML), Montreal 2009
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A Factor Model for Learning Higher Order Features in Natural Images by Yan Karklin, 2009
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Matrix Computations in Machine Learning by Inderjit S. Dhillon, 2009
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Tutorial on Learning Deep Architectures by Yoshua Bengio, Yann LeCun, 2009
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Unsupervised Discovery of Structure, Succinct Representations and Sparsity by Andrew Y. Ng, 2009
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Autumn School 2006: Machine Learning over Text and Images - Pittsburgh
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Semisupervised Learning Approaches by Tom Mitchell, 2007 (rec 2006)
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CERN Summer School 2009
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Introduction to Statistics by Glen Cowan, 2010 (rec 2009)
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CERN Summer School 2010
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Introduction to Statistics by Glen Cowan, 2011 (rec 2010)
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EPSRC Winter School in Mathematics for Data Modelling, Sheffield 2008
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Introduction to Support Vector Machines by Colin Campbell, 2008
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Gaussian Processes in Practice Workshop, Bletchley Park 2006
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Gaussian Process Basics by David MacKay, 2007 (rec 2006)
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MIT World Host: Computer Science and Artificial Intelligence Laboratory (CSAIL)
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Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind by Marvin Minsky, 2011 (rec 2007)
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Machine Learning Summer School (MLSS), Berder Island 2004
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Advanced Statistical Learning Theory by Olivier Bousquet, 2007 (rec 2004)
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Markov Chain Monte Carlo Methods by Christian Robert, 2007 (rec 2004)
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Statistical Learning Theory by John Shawe-Taylor, 2007 (rec 2004)
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Machine Learning Summer School (MLSS), Bordeaux 2011
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Convex Optimization by Lieven Vandenberghe, 2011
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Graphical Models and message-passing algorithms by Martin J. Wainwright, 2011
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Kernel Methods by Bernhard Schölkopf, 2011
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Learning Theory: statistical and game-theoretic approaches by Nicolò Cesa-Bianchi, 2011
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Monte Carlo Methods by Arnaud Doucet, 2011
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Machine Learning Summer School (MLSS), Cambridge 2009
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Approximate Inference by Tom Minka, 2009
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Bayesian or Frequentist, Which Are You? by Michael I. Jordan, 2009
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Computer Vision by Andrew Blake, 2009
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Deep Belief Networks by Geoffrey E. Hinton, 2009
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Gaussian Processes by Carl Edward Rasmussen, 2009
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Information Theory by David MacKay, 2009
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Introduction To Bayesian Inference by Christopher Bishop, 2009
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Learning Theory by John Shawe-Taylor, 2009
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Markov Chain Monte Carlo by Iain Murray, 2009
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Reinforcement Learning by Michael Littman, 2009
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Machine Learning Summer School (MLSS), Canberra 2005
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Gradient Methods for Machine Learning by Nicol Schraudolph, 2007 (rec 2005)
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Machine Learning Summer School (MLSS), Canberra 2006
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Rapid Stochastic Gradient Descent: Accelerating Machine Learning by Nicol Schraudolph, 2007 (rec 2006)
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Machine Learning Summer School (MLSS), Canberra 2010
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Online Learning by Peter L. Bartlett, 2011 (rec 2010)
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Machine Learning Summer School (MLSS), Chicago 2005
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Boosting by Robert Schapire, 2007 (rec 2005)
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Energy-based models & Learning for Invariant Image Recognition by Yann LeCun, 2007 (rec 2005)
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Trees for Regression and Classification by Robert Nowak, 2007 (rec 2005)
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Machine Learning Summer School (MLSS), Chicago 2009
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Bounding Excess Risk in Machine Learning by Vladimir Koltchinskii, 2009
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Fitting a Graph to Vector Data by Daniel A. Spielman, 2009
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How to Visualize the Unseeable by Xiaochuan Pan, 2009
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Kernel Methods and Support Vector Machines by John Shawe-Taylor, 2009
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Learning Dictionaries for Image Analysis and Sensing by Guillermo Sapiro, 2009
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Learning Feature Hierarchies by Yann LeCun, 2009
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Matrix Completion via Convex Optimization: Theory and Algorithms by Emmanuel Candes, 2009
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Optimization Algorithms in Support Vector Machines by Stephen J. Wright, 2009
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Seeking Interpretable Models for High Dimensional Data by Bin Yu, 2009
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Semi-Supervised Learning by Jerry (Xiaojin) Zhu, 2009
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Sparse Representations from Inverse Problems to Pattern Recognition by Stéphane Mallat, 2009
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Theory and Applications of Boosting by Robert Schapire, 2009
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Unsupervised Learning for Stereo Vision by David McAllester, 2009
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Machine Learning Summer School (MLSS), Kioloa 2008
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Foundations of Machine Learning by Marcus Hutter, 2008
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Introduction to Reinforcement Learning by Csaba Szepesvari, 2008
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Introduction to Statistical Machine Learning by Marcus Hutter, 2008
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Kernel methods and Support Vector Machines by Alexander J. Smola, 2008
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Learning in Computer Vision by Simon Lucey, 2008
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Monte Carlo Simulation for Statistical Inference, Model Selection and Decision Making by Nando de Freitas, 2008
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Machine Learning Summer School (MLSS), Taipei 2006
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Introduction to Boosting by Gunnar Rätsch, 2007 (rec 2006)
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Machine Learning, Probability and Graphical Models by Sam Roweis, 2007 (rec 2006)
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Support Vector Machines by Chih-Jen Lin, 2007 (rec 2006)
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Machine Learning Summer School (MLSS), Tübingen 2003
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Empirical Inference by Vladimir Vapnik, 2007 (rec 2003)
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Some Mathematical Tools for Machine Learning by Chris Burges, 2007 (rec 2003)
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Machine Learning Summer School (MLSS), Tübingen 2007
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Introduction to kernel methods by Alexander Smola, Bernhard Scholkopf, 2007
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Machine Learning seminars at the Cambridge University Engineering Department
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Group Theory and Machine Learning by Risi Kondor, 2008 (rec 2007)
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NATO Advanced Study Institute on Mining Massive Data Sets for Security
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Foundations of Statistical Learning Theory - Empirical Inference in high-dimention spaces by Léon Bottou, Vladimir Vapnik, 2007
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Learning using Many Examples by Léon Bottou, 2007
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NIPS Workshop on Dynamical Systems, Stochastic Processes and Bayesian Inference, Whistler 2006
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A Tutorial Introduction to Stochastic Differential Equations: Continuous-time Gaussian Markov Processes by Chris Williams, 2007 (rec 2006)
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NIPS Workshop on Efficient Machine Learning, Whistler 2007
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Interview with Yann LeCun by Yann LeCun, 2008 (rec 2007)
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New Quasi-Newton Methods for Efficient Large-Scale Machine Learning by S.V.N. Vishwanathan, 2007
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Speeding Up Stochastic Gradient Descent by Yoshua Bengio, 2007
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Who is Afraid of Non-Convex Loss Functions? by Yann LeCun, 2007
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NIPS Workshop on Learning to Compare Examples, Whistler 2006
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Learning Similarity Metrics with Invariance Properties by Yann LeCun, 2007 (rec 2006)
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Neighbourhood Components Analysis and Metric Learning by Sam Roweis, 2007 (rec 2006)
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NIPS Workshop on Optimization for Machine Learning, Whistler 2008
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Large-scale Machine Learning and Stochastic Algorithms by Léon Bottou, 2008
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NIPS Workshops, Sierra Nevada 2011
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Cosmology meets Machine Learning
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Efficient Estimation of N-point Spatial Statistics by Alexander G. Gray, 2012 (rec 2011)
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Optimization for Machine Learning
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Efficiency of Quasi-Newton Methods on Strictly Positive Functions by Yurii Nesterov, 2011 (rec 2010)
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Fast first-order methods for convex optimization with line search by Katya Scheinberg, 2012 (rec 2011)
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Limited-memory quasi-Newton and Hessianfree Newton methods for non-smooth optimization by Mark Schmidt, 2011 (rec 2010)
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Lock-Free Approaches to Parallelizing Stochastic Gradient Descent by Benjamin Recht, 2012 (rec 2011)
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Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization by Ohad Samir, 2012 (rec 2011)
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Relations between machine learning problems – an approach to unify the field
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Relations Betweeen Machine Learning Problems by Robert C. Williamson, 2012 (rec 2011)
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PASCAL Bootcamp in Machine Learning, Marseille 2010
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Introduction to Machine Learning by Iain Murray, 2010
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PASCAL Bootcamp in Machine Learning, Vilanova 2007
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Basics of probability and statistics by Mikaela Keller, 2007
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Single Lectures Series
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A tutorial on Deep Learning by Geoffrey E. Hinton, 2009
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Learning Deep Hierarchies of Representations by Yoshua Bengio, 2009
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Summer Schools in Logic and Learning, Canberra 2009
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Computer vision by Richard Hartley, 2009
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Graphical models by Tibério Caetano, 2009
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Group Theory in Machine Learning by Marconi Barbosa, 2009
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Learning Theory by Mark Reid, 2009
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Reinforcement learning by Scott Sanner, 2009
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The 13th International Conference on Knowledge Discovery and Data Mining
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From Trees to Forests and Rule Sets - A Unified Overview of Ensemble Methods by John Elder, Giovanni Seni, 2007
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Learning Bayesian Networks by Richard E. Neapolitan, 2007
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The Analysis of Patterns, Bertinoro 2007
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Support Vector Machines and Kernel Methods by Colin Campbell, 2007
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![Computer Science and Machine Learning Conferences [2003-2011, ENG]](/static/img/no-torrent-image.png "Computer Science and Machine Learning Conferences [2003-2011, ENG]")
