Machine Learning Summer School (MLSS), Taipei 2006
31 Lectures · Jul 23, 2006
About
The two-week summer school program consists of about 40+ hours of lectures from 7/24 to 8/4. The late afternoon sessions will provide a chance for the participants to discuss the latest research problems with the invited speakers. Prior to the summer school, the National Taiwan University of Science and Technology will offer a month-long machine learning summer course for graduate credits. You may choose part of them that fit your need or attend everything if you are sufficiently energetic.
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Lectures
Machine Learning Reductions
Feb 25, 2007
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Neighbourhood Components Analysis
Feb 25, 2007
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Triple jump acceleration for the EM algorithm and its extrapolation-based varian...
Feb 25, 2007
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Reinforcement Learning Theory
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Agnostic Active Learning
Feb 25, 2007
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Large-Margin Thresholded Ensembles for Ordinal Regression
Feb 25, 2007
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Predictive methods for Text mining
Feb 25, 2007
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Graphical Models, Variational Methods, and Message-Passing
Feb 25, 2007
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Regularization of Kernel Methods by Decreasing the Bandwidth of the Gaussian Ker...
Jun 11, 2007
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Large Scale Ranking Problem: some theoretical and algorithmic issues
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Intel Research : User Activity based Adaptive Power Management (APM)
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Piecewise 1-Dim Self Organizing Map P1D-SOM
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Support Vector Machines
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Kernel Methods in Computational Biology
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Ranking Individuals by Group Comparisons
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Protein Subcellular Localization Prediction Based on Compartment-Specific Biolog...
Feb 25, 2007
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L1-based relaxations for sparsity recovery and graphical model selection in the ...
Feb 25, 2007
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Online Learning and Bregman Divergences
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11740 Views
Unifying Divergence Minimization and Statistical Inference via Convex Duality
Feb 25, 2007
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Identifying Temporal Patterns and Key Players in Document Collections
Feb 25, 2007
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4727 Views
Facial expression recognition and emotion recognition from speech
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15026 Views
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimensi...
Feb 25, 2007
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Splice form prediction using Machine Learning
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Kernels on histograms through the transportation polytope
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Pattern Classification and Large Margin Classifiers
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Clustering - An overview
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Introduction to Boosting
Feb 25, 2007
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The stability of a good clustering
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A Bayesian Probability Calculus for Density Matrices
Feb 25, 2007
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AdaBoost is Universally Consistent
Feb 25, 2007
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Ranking by Stealing Human Cycles
Feb 25, 2007
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