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Events for August 01, 2014
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Six Sigma Black BeltFri, Aug 01, 2014
DEN@Viterbi, Executive Education
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 youââ¬â¢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
Contact: 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
Contact: 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=925b9f53d4eb4964a37af20bacde2ad31dLocation: Information Science Institute (ISI) - 1135
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=925b9f53d4eb4964a37af20bacde2ad31d
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
