9.520 Statistical Learning Theory and Applications
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9.520 Statistical Learning Theory and Applications

        

9.520 Statistical Learning Theory and Applications

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This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also... More
Material Type: Online Course
Date Added to MERLOT: June 09, 2011
Date Modified in MERLOT: June 09, 2011
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Mobile Compatibility: Not specified at this time
Language: English
Cost Involved: no
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Creative Commons: Creative Commons License
This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States

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