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ICCV is the premier international Computer Vision event comprising the main ICCV conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

This course is intended to provide a practical introduction to computational problem solving. Topics covered include: the notion of well-conditioned and poorly conditioned problems, with examples drawn from linear algebra; the concepts of forward and backward stability of an algorithm, with examples drawn from floating point arithmetic and linear-algebra; basic techniques for the numerical solution of linear and nonlinear equations, and for numerical optimization, with examples taken from linear algebra and linear programming; principles of numerical interpolation, differentiation and integration, with examples such as splines and quadrature schemes; an introduction to numerical methods for solving ordinary differential equations, with examples such as multistep, Runge Kutta and collocation methods, along with a basic introduction of concepts such as convergence and linear stability; An introduction to basic matrix factorizations, such as the SVD; techniques for computing matrix factorizations, with examples such as the QR method for finding eigenvectors; Basic principles of the discrete/fast Fourier transform, with applications to signal processing, data compression and the solution of differential equations.

by Professor SR Varadham. Course slides are available at http://www.math.nyu.edu/faculty/varadhan/Fall2013/

There are strong interactions between learning algorithms which estimate the parameters of a model from data, and inference algorithms which use a model to make predictions about data. Understanding the intricacies of these interactions is crucial for advancing the state-of-the-art on real-world tasks in natural language processing, computer vision, computation biology, etc. Yet, many facets of these interactions remain unknown. In this workshop, we study the interactions between inference and learning using two reciprocating perspectives.

The 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.