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  • NYU Course on Deep Learning (Spring 2014)

    This is a graduate course on deep learning, one of the hottest topics in machine learning and AI at the moment. In the last two or three years, Deep learning has revolutionized speech recognition and image recognition. Deep learning is widely deployed by such companies as Google, Facebook, Microsoft, IBM, Baidu, Apple and others for audio/speech, image, video, and natural language processing.

  • LxMLS 2013

    LxMLS 2013 took place July 24-31 at Instituto Superior Técnico, a leading Engineering and Science school in Portugal.

  • FOCS 2013

    The 54th Annual Symposium on Foundations of Computer Science (FOCS 2013), sponsored by the IEEE Computer Society Technical Committee on Mathematical Foundations of Computing, was held in Berkeley, California on October 27–29, 2013 (Sunday through Tuesday).

  • WritersUA East 2013

    The Conference for Software User Assistance was held in Newport, RI, October 27-29, 2013.

  • ICCV 2013

    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.

  • NYU Course on Scientific Computing

    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.

  • NYU Course on Statistical and Mathematical Methods

    by Professor SR Varadham. Course slides are available at

  • Inferning 2013

    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.