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  • WritersUA West 2014

    The Conference for Software User Assistance was held in Seattle, WA.

  • NYU Course on Machine Learning and Computational Statistics 2014

    Machine learning is an exciting and fast-moving field at the intersection of computer science, statistics, and optimization with many recent consumer applications (e.g., Microsoft Kinect, Google Translate, Iphone's Siri, digital camera face detection, Netflix recommendations, Google news). Machine learning and computational statistics also play a central role in data science. In this graduate-level class, students will learn about the theoretical foundations of machine learning and computational statistics and how to apply these to solve new problems. This is a required course for the MS in Data Science and should be taken in the first year of study; it is also suitable for MS and Ph.D. students in Computer Science and related fields (see pre-requisites below).

  • NYU Course on Big Data (Spring 2014)

    Big Data requires the storage, organization, and processing of data at a scale and efficiency that go well beyond the capabilities of conventional information technologies. In this course, we will study the state of the art in big data management: we will learn about algorithms, techniques and tools needed to support big data processing. In addition, we will examine real applications that require massive data analysis and how they can be implemented on Big Data platforms. The course will consist of lectures based both on textbook material and scientific papers. It will also include programming assignments that will provide students with hands-on experience on building data-intensive applications using existing Big Data platforms, including Amazon AWS. Besides lectures given by the instructor, we will also have guest lectures by experts in some of the topics we will cover.

  • 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.