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Statistical Learning Theory and Applications

9.520. Statistical Learning Theory and Applications. Jake Bouvrie and Lorenzo Rosasco and Ryan Rifkin + tomaso poggio. Class 25: Project presentations. 9.520 Statistical Learning Theory and Applications. 10:30 - Charles Frogner “Diffusion maps” - Vikash Mansinghka “???”

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Statistical Learning Theory and Applications

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  1. 9.520 Statistical Learning Theory and Applications Jake Bouvrie and Lorenzo Rosasco and Ryan Rifkin + tomaso poggio

  2. Class 25: Project presentations 9.520 Statistical Learning Theory and Applications 10:30 - Charles Frogner “Diffusion maps” - Vikash Mansinghka “???” - Dan Roy “online, semi-supervised learning“ or "on the representational power of deep belief networks” - Neil Herriot “Learning: Tikhonov vs hierarchical models” - Charles McBrearty “GPU’s based implementation of SVM classifiers” - Ayan Chakrabarti “Core Vector Machines”

  3. Class 26: Project presentations 9.520 Statistical Learning Theory and Applications • 10:30 - Simon Laflamme “Online Learning Algorithm for Structural Control using Magnetorheological Actuators” • - Emily Shen “Time series prediction” • - Zak Stone “Facebook project” • - Jeff Miller “Clustering features in the standard model of cortex” • - Manuel Rivas "Learning Age from Gene Expression Data“ • - Demba Ba “Sparse Approximation of the Spectrogram via Matching Pursuits: Applications to Speech Analysis” • - Nikon Rasumov "Data mining in controlled environment and real data"

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