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In this introductory course we will discuss how log linear models can be extended to feature space. These log linear models have been studied by statisticians for a long time under... Watch Video
The ability to evaluate nonlinear function classes rapidly is crucial for nonparametric estimation. We propose an improvement to random kitchen sinks that offers O(n log d) computation... Watch Video
Recent work on collaborative filtering has led to a large number of both scalable and theoretically well founded algorithms. In this paper, we show that collaborative filtering and... Watch Video
Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful learning approach... Watch Video
This lecture given by Mr. Smola is combined with Mr. Bernhard Schoelkopf and will encopass Part 1, Part 5, Part 6 of the complete lecture. \\ Part 2, 3 and 4 of this lecture can be... Watch Video
In this short course I will discuss exponential families, density estimation, and conditional estimators such as Gaussian Process classification, regression, and conditional random... Watch Video
The tutorial will introduce the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. This includes a derivation of the support vector... Watch Video
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