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Events Calendar



Events for August 01, 2014

  • Repeating EventSix Sigma Black Belt

    Fri, Aug 01, 2014

    Distance Education Network

    Conferences, Lectures, & Seminars


    Speaker: TBD,

    Abstract: Event Dates:
    Week 1: July 7 - 11, 2014 from 9:00am - 5:00pm

    Week 2: August 11 - 15, 2014 from 9:00am - 5:00pm

    Week 3: September 8 - 12, 2014 from 9:00am - 5:00pm

    This course teaches you the advanced problem-solving skills you will need in order to measure a process, analyze the results, develop process improvements and quantify the resulting savings. Project assignments between sessions require you to apply what you’ve learned. This course is presented in three five-day sessions over a three-month period.

    Learn the advanced problem-solving skills you need to implement the principles, practices and techniques of Six Sigma to maximize performance and cost reductions in your organization. During this three-week practitioner course, you will learn how to measure a process, analyze the results, develop process improvements and quantify the resulting savings. You will be required to complete a project demonstrating mastery of appropriate analytical methods and pass an examination to earn USC and IIE's Six Sigma Black Belt Certificate. This practitioner course for Six Sigma implementation provides extensive coverage of the Six Sigma process as well as intensive exposure to the key analytical tools associated with Six Sigma, including project management, team skills, cost analysis, FMEA, basic statistics, inferential statistics, sampling, goodness of fit testing, regression and correlation analysis, reliability, design of experiments, statistical process control, measurement systems analysis and simulation. Computer applications are emphasized.

    More Info


    Host: Professional Programs

    Audiences: Registered Attendees

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    Posted By: Viterbi Professional Programs

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  • Meet USC: Admission Presentation, Campus Tour, & Engineering Talk

    Fri, Aug 01, 2014

    Viterbi School of Engineering Undergraduate Admission

    Receptions & Special Events


    This half day program is designed for prospective freshmen and family members. Meet USC includes an information session on the University and the Admission process; a student led walking tour of campus and a meeting with us in the Viterbi School. Meet USC is designed to answer all of your questions about USC, the application process and financial aid. Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m. Please visit https://esdweb.esd.usc.edu/unresrsvp/MeetUSC.aspx to check availability and make an appointment. Be sure to list an Engineering major as your "intended major" on the webform!

    Location: Ronald Tutor Campus Center (TCC) -

    Audiences: Everyone Is Invited

    Posted By: Viterbi School of Engineering Undergraduate Admission

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  • AI SEMINAR-Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods

    Fri, Aug 01, 2014 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Jascha Sohl-Dickstein, Kahn Academy, Stanford University

    Talk Title: Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods

    Series: AISeminar

    Abstract: Abstract:
    I will present an algorithm for performing minibatch optimization that combines the computational efficiency of stochastic gradient descent (SGD) with the second order curvature information leveraged by quasi-Newton methods. These approaches are unified by maintaining an independent Hessian approximation for each minibatch. Each update step requires only a single minibatch evaluation (as in SGD), and each step is scaled using an approximate inverse Hessian and little to no adjustment of hyperparameters is required (as is typical for quasi-Newton methods). This algorithm is made tractable in memory and computational cost even for high dimensional optimization problems by storing and manipulating the quadratic approximations for each minibatch in a shared, time evolving, low dimensional subspace. Experimental results demonstrate improved convergence on seven diverse optimization problems. The algorithm is released as open source Python and MATLAB packages.

    Optimizer available at:
    https://github.com/Sohl-Dickstein/Sum-of-Functions-Optimizer

    Paper reference:
    Jascha Sohl-Dickstein, Ben Poole, and Surya Ganguli
    Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods
    International Conference on Machine Learning (2014)
    http://arxiv.org/abs/1311.2115



    Biography: Bio:
    Jascha Sohl-Dickstein is an Academic Resident at the Khan Academy, and a visiting scholar in applied physics in Surya Ganguli's lab at Stanford University. He earned his PhD in 2012 in the Redwood Center for Theoretical Neuroscience at UC Berkeley, in Bruno Olshausen's lab. His research interests involve applying ideas from statistical physics and dynamical systems to problems in machine learning and neuroscience.

    Host: Greg Ver Steeg

    Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=925b9f53d4eb4964a37af20bacde2ad31

    Location: Information Science Institute (ISI) - 1135

    WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=925b9f53d4eb4964a37af20bacde2ad31d

    Audiences: Everyone Is Invited

    Posted By: Alma Nava / Information Sciences Institute

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