PPT-Cross-State Learning and Good Data

Kathy Hebbeler SRI International OSEP Leadership Meeting 2016 2 What is DaSy A technical assistance TA center funded by OSEP to improve Part C and Part B preschool data by helping states Build better data systems. :. An . Empirical Study. Gary M. Weiss. Alexander . Battistin. Fordham University. Motivation. Classification performance related to amount of training data. Relationship visually represented by learning curve. Cross State Convening. 6/24/15. The First Day of School by Richard A. Lawson.  . And what are the important questions anyway. On this first day of school, after a night of no sleep. Re-launching the Challenger. 1. . Urgency to reflect . on old. s. tory and create a . n. ew narrative around . success. 3. Making . adults . feel respected. 2. . Set . non-. negotiables. 4. Well . defined standards. Osamu Iwamoto. Japan Atomic Energy Agency. 2010 Symposium on Nuclear Data. Applications of nuclear data. nucleosynthesis. JRR-3. J-PARC. ADS. soft error. (. 株. ). 化研提供. medical application. OPPORTUNITIES AND PITFALLS. What I’m going to talk about. Extremely broad topic – will keep it high level. Why and how you might use ML. Common pitfalls – not ‘classic’ data science. Some example applications and algorithms that I like. Vasilis Syrgkanis. Microsoft Research, New England. Points of interaction. Mechanism design and analysis for learning agents. Online learning as behavioral model in auctions. Learning good mechanisms from data. Slides for Chapter . 5, Evaluation. . of . Data Mining. by I. H. Witten, E. . Frank, . M. A. . Hall and C. J. Pal. 2. Credibility: Evaluating what’s been learned. Issues: training, testing, tuning. South Dakota. Cross-State Convening June 2017. H325A120003. Session Goals . Participants will leave the session with strategies to mitigate typical barriers to collaborative efforts, within or across institutions. Auburn University. Wednesday, September 13, 2017. IOM 530: Intro. to Statistical Learning . 1. Outline. Cross Validation. The Validation Set Approach. Leave-One-Out Cross Validation. K-fold Cross Validation. First Lecture Today (Tue 19 Jul). Read Chapter 18.1-18.4. Second Lecture Today . (Tue 19 Jul). Read . Chapters 18.5-12. , 20.1-2. Next Lecture (Thu 21 Jul). Final Exam Review. (Please read lecture topic material before and after each lecture on that topic). Goals of Weeks 5-6. What is machine learning (ML) and when is it useful?. Intro to major techniques and applications. Give examples. How can CUDA help?. Departure from usual pattern: we will give the application first, and the CUDA later. CS 179: Lecture 13 Intro to Machine Learning Goals of Weeks 5-6 What is machine learning (ML) and when is it useful? Intro to major techniques and applications Give examples How can CUDA help? Departure from usual pattern: we will give the application first, and the CUDA later small energies. Hartmut. Machner, FZ . Jülich. and Univ. Duisburg-Essen. Frank . Hinterberger. , Univ. Bonn. Regina . Siudak. , PAN Krakow. for the . HIRES & GEM . collaborations. Why. . is. . Optimization. . Algorithms. . over . Graphs. Hanjun. . Dai. *. Joint work with . Elias. . Khalil. *, . Yuyu. Zhang, . Bistra. . Dilkina. , Le Song. Georgia Tech. To appear in NIPS 2017. * . equal contribution.