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Events for July 25, 2014
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Six Sigma Black BeltFri, Jul 25, 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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AI Seminar-Fast algorithms for nearest neighbor classification
Fri, Jul 25, 2014 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Sanjoy Dasgupta, UCSD
Talk Title: Fast Algorithms for Nearest Neighbor Classification
Series: Artificial Intelligence Seminar
Abstract: Nearest neighbor (NN) search is one of the simplest and most enduring methods of statistical estimation. We examine its algorithmic complexity via two results.
1. Randomized tree structures for fast NN search
The k-d tree was one of the first spatial data structures proposed for NN search. Its efficacy is diminished in high-dimensional spaces, but several variants, with randomization and overlapping cells, have proved to be successful in practice. We analyze three such schemes. We show that the probability that they fail to find the nearest neighbor, for any data set and any query point, is directly related to a simple potential function that captures the difficulty of the point configuration. We then bound this potential function in several situations of interest: when the data are drawn from a doubling measure; when the data and query distributions are identical and are supported on a set of bounded doubling dimension; and when the data are documents from a topic model.
2. Data structures that adapt to a query distribution
Can we leverage learning techniques to build a fast NN retrieval data structure? We present a general learning framework for the NN problem in which sample queries are used to learn the parameters of a data structure that minimize the retrieval time and/or the miss rate. We explore the potential of this framework through two popular NN data structures: k-d trees and the rectilinear structures employed by locality sensitive hashing. We derive a generalization theory for these data structure classes and present simple learning algorithms for both. Experimental results reveal that learning often improves on the already strong performance of these data structures.
This is joint work with Lawrence Cayton, Eugene Che, Kaushik Sinha, and Zhen Zhai.
Biography: Sanjoy Dasgupta is a Professor in the Department of Computer Science and Engineering at UC San Diego. He received his PhD from Berkeley in 2000, and spent two years at AT&T Research Labs before joining UCSD. His area of research is algorithmic statistics, with a focus on unsupervised and minimally supervised learning. He is the author of a textbook, "Algorithms" (with Christos Papadimitriou and Umesh Vazirani), that appeared in 2006.
Home Page:
http://cseweb.ucsd.edu/users/dasgupta/
Host: Greg Ver Steeg
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=84af215b6be04e48b5f164352b9f20e31dLocation: 11th Flr Conf Rm # 1135, Marina Del Rey
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=84af215b6be04e48b5f164352b9f20e31d
Audiences: Everyone Is Invited
Contact: Peter Zamar
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NL Seminar- Navigation Dynamics in Networks
Fri, Jul 25, 2014 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Daniel Lamprecht, Graz University of Technology
Talk Title: Navigation Dynamics in Networks
Series: Natural Language Seminar
Abstract: Research on networks has already revealed much about the structure of real-world networks. Network dynamics such as navigation or exploration, however, are something less well-researched. Yet, we constantly design and use networked systems meant for navigation and exploration. In this talk, I will present a short overview of what we know about navigability, followed by the our work on exploring dynamics occurring on recommendation networks - networks formed implicitly by recommender systems. Navigability can serve as an evaluation criterion for recommender systems and reveal to what extent a system supports navigation and exploration. Based on analysis of topology and dynamical processes, we find that current systems do not support navigation very well, and propose techniques to overcome this.
Biography: Daniel Lamprecht is a PhD student at Graz University of Technology and is interning at ISI this summer. His research explores network science, web science and recommender systems and especially focuses on network navigability. This summer, he's working with Kristina Lerman on navigation dynamics and click biases in Wikigames. In the past, he has also studied navigation dynamics in information networks with the aid of biomedical ontologies.
Host: Aliya Deri and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - Conference Room # 1135, Marina del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
