Report of Contributions
|
|
|
- Jonas Day
- 8 years ago
- Views:
Transcription
1 Heavy Flavour Data Mining workshop Report of Contributions
2 Heavy Flavour... / Report of Contributions Welcome Contribution ID : 1 Welcome Thursday, 18 February :00 (10) June 15, 2016 Page 1
3 Heavy Flavour... / Report of Contributions Classifiers for centrality determ... Contribution ID : 2 Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions Friday, 19 February :40 (20) Primary author(s) : Igor Altsybeev (St. Petersburg State University (RU)) Co-author(s) : Vladimir Kovalenko (St. Petersburg State University (RU)) Presenter(s) : Igor Altsybeev (St. Petersburg State University (RU)) Session Classification : Data Science Applications June 15, 2016 Page 2
4 Heavy Flavour... / Report of Contributions of «Flavors of Physic... Contribution ID : 3 of «Flavors of Physics» Challenge Thursday, 18 February :50 (40) Presenter(s) : Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) Session Classification : HEP challenges June 15, 2016 Page 3
5 Heavy Flavour... / Report of Contributions Classifier output calibration to... Contribution ID : 4 Classifier output calibration to probability Friday, 19 February :00 (40) Presenter(s) : Tatiana Likhomanenko (National Research Centre Kurchatov Institute (RU)) Session Classification : Data Science Applications June 15, 2016 Page 4
6 Heavy Flavour... / Report of Contributions Data Fusion Surogate Modeling... Contribution ID : 6 Data Fusion Surogate Modeling on Incomplete Factorial Design of Experiments Friday, 19 February :00 (40) Presenter(s) : Eugene Burnaev (IITP) Session Classification : Data Science Applications June 15, 2016 Page 5
7 Heavy Flavour... / Report of Contributions Boosting applications for HEP Contribution ID : 7 Boosting applications for HEP Thursday, 18 February :20 (90) Presenter(s) : Aleksei Rogozhnikov (Yandex School of Data Analysis (RU)) Session Classification : Machine Learning tools & tutorials June 15, 2016 Page 6
8 Heavy Flavour... / Report of Contributions An introduction to machine lea... Contribution ID : 9 An introduction to machine learning with Scikit-Learn Thursday, 18 February :00 (120) Presenter(s) : Gilles Louppe (New York University (US)) Session Classification : Machine Learning tools & tutorials June 15, 2016 Page 7
9 Heavy Flavour... / Report of Contributions Reproducible Experiment Platf... Contribution ID : 10 Reproducible Experiment Platform & Everware Friday, 19 February :30 (120) Presenter(s) : Aleksei Rogozhnikov (Yandex School of Data Analysis (RU)); Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) Session Classification : Machine Learning tools & tutorials June 15, 2016 Page 8
10 Heavy Flavour... / Report of Contributions Mathematics of Big Data Contribution ID : 11 Mathematics of Big Data Friday, 19 February :00 (40) Presenter(s) : Dmitry Vetrov (Skoltech, Yandex School of Data Analysis, Higher School of Economics) Session Classification : Data Science Applications June 15, 2016 Page 9
11 Heavy Flavour... / Report of Contributions Efficient Elastic Net Regulariza... Contribution ID : 12 Efficient Elastic Net Regularization for Sparse Linear Models in the Multilabel Setting Friday, 19 February :40 (30) Presenter(s) : Zachary Chase Lipton (University of California, Amazon) Session Classification : Data Science Applications June 15, 2016 Page 10
12 Heavy Flavour... / Report of Contributions Data Doping solution for "Flav... Contribution ID : 13 Data Doping solution for "Flavours of Physics" challenge Thursday, 18 February :50 (40) Presenter(s) : Vicens Gaitan Session Classification : HEP challenges June 15, 2016 Page 11
13 Heavy Flavour... / Report of Contributions Transfer Learning solution for... Contribution ID : 14 Transfer Learning solution for "Flavours of Physics" challenge Thursday, 18 February :30 (20) Presenter(s) : Alexander Rakhlin Session Classification : HEP challenges June 15, 2016 Page 12
14 Heavy Flavour... / Report of Contributions OpenML: Collaborative machi... Contribution ID : 15 OpenML: Collaborative machine learning Friday, 19 February :00 (40) Today, the ubiquity of the internet is allowing new, more scalable forms of scientific collaboration. Networked science tools allow scientists to share and organize data on a global scale, build directly on each other s data and techniques, reuse them in unforeseen ways, and mine all data to search for patterns. OpenML.org is a place for researchers to analyse data together, building on shared data sets, machine learning code and prior experiments. Integrated in many machine learning environments, it helps researchers win time by automating reproducible sharing, reuse and experimentation as much as possible. It also helps scientists and students across scientific fields to explore the latest and most relevant open data sets and machine learning techniques, find out which are most useful in their work, collaborate with others online, and gain more credit for their work by making it more visible and easily reusable. Presenter(s) : Joaquin Vanschoren ( Session Classification : Machine Learning tools & tutorials June 15, 2016 Page 13
15 Heavy Flavour... / Report of Contributions Data Science at LHCb Contribution ID : 16 Data Science at LHCb Thursday, 18 February :10 (40) Presenter(s) : Tim Head (Ecole Polytechnique Federale de Lausanne (CH)) Session Classification : HEP challenges June 15, 2016 Page 14
16 Heavy Flavour... / Report of Contributions Closing Remarks Contribution ID : 17 Closing Remarks Saturday, 20 February :50 (10) Presenter(s) : Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)); Marcin Chrzaszcz (Universitaet Zuerich (CH), Institute of Nuclear Physics (PL)) June 15, 2016 Page 15
17 Heavy Flavour... / Report of Contributions of open space discussions Contribution ID : 18 of open space discussions Saturday, 20 February :20 (20) Presenter(s) : Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) Session Classification : Data Science Applications June 15, 2016 Page 16
18 Heavy Flavour... / Report of Contributions TensorFlow introduction & tut... Contribution ID : 19 TensorFlow introduction & tutorial #1 Saturday, 20 February :00 (30) Primary author(s) : Rafal Jozefowicz (Google) Presenter(s) : Rafal Jozefowicz (Google) Session Classification : Machine Learning tools & tutorials June 15, 2016 Page 17
19 Heavy Flavour... / Report of Contributions Pitfalls of evaluating a classifie... Contribution ID : 20 Pitfalls of evaluating a classifier s performance in high energy physics applications Thursday, 18 February :50 (30) Presenter(s) : Gilles Louppe (New York University (US)) Session Classification : HEP challenges June 15, 2016 Page 18
20 Heavy Flavour... / Report of Contributions Optimized Methods to Apply... Contribution ID : 22 Optimized Methods to Apply Neural Networks in HEP Friday, 19 February :10 (20) Primary author(s) : Lev Dudko (M.V. Lomonosov Moscow State University (RU)) Presenter(s) : Lev Dudko (M.V. Lomonosov Moscow State University (RU)) Session Classification : Data Science Applications June 15, 2016 Page 19
21 Heavy Flavour... / Report of Contributions Automatic Tuning of Hyperpar... Contribution ID : 25 Automatic Tuning of Hyperparameters Saturday, 20 February :00 (40) Primary author(s) : Alexander Fonarev (Skoltech) Presenter(s) : Alexander Fonarev (Skoltech) Session Classification : Data Science Applications June 15, 2016 Page 20
22 Heavy Flavour... / Report of Contributions Calibration curves as tool to te... Contribution ID : 26 Calibration curves as tool to test for over- and underfitting Friday, 19 February :10 (20) Primary author(s) : Artem Vorozhtsov (Yandex) Presenter(s) : Artem Vorozhtsov (Yandex) Session Classification : Machine Learning tools & tutorials June 15, 2016 Page 21
23 Heavy Flavour... / Report of Contributions Fast multimodal clustering: se... Contribution ID : 27 Fast multimodal clustering: searching for optimal patterns Saturday, 20 February :40 (40) Primary author(s) : Dmitry Ignatov (HSE) Presenter(s) : Dmitry Ignatov (HSE) Session Classification : Data Science Applications June 15, 2016 Page 22
24 Heavy Flavour... / Report of Contributions Deep Learning for event recons... Contribution ID : 28 Deep Learning for event reconstruction Saturday, 20 February :40 (40) Presenter(s) : Amir Farbin (University of Texas at Arlington (US)) Session Classification : Data Science Applications June 15, 2016 Page 23
25 Heavy Flavour... / Report of Contributions TensorFlow introduction & tut... Contribution ID : 30 TensorFlow introduction & tutorial. Continuation Saturday, 20 February :30 (80) Presenter(s) : Rafal Jozefowicz (Google) Session Classification : Machine Learning tools & tutorials June 15, 2016 Page 24
26 Heavy Flavour... / Report of Contributions Nvidia tutorial Contribution ID : 31 Nvidia tutorial Saturday, 20 February :50 (10) Primary author(s) : Alison Lowndes (NVIDIA) Session Classification : Machine Learning tools & tutorials June 15, 2016 Page 25
Head of Laboratory at National Research University Higher School of Economics andrey.u@gmail.com
Andrey Ustyuzhanin Head of Laboratory at National Research University Higher School of Economics andrey.u@gmail.com Summary Researcher, data scientist, software developer. Has extensive experience working
Collective Behavior Prediction in Social Media. Lei Tang Data Mining & Machine Learning Group Arizona State University
Collective Behavior Prediction in Social Media Lei Tang Data Mining & Machine Learning Group Arizona State University Social Media Landscape Social Network Content Sharing Social Media Blogs Wiki Forum
POSTGRAD PLACEMENTS. Placements are an integral part of the Masters programmes, so international students will not require additional work visas.
POSTGRAD PLACEMENTS COMPUTATIONAL FINANCE DATA SCIENCE AND ANALYTICS MACHINE LEARNING KEY INFORMATION Placements can start in the middle of June 2015 or later and must finish by the middle of June 2016
MATCHDAY 1 7-9 September 2014
MATCHDAY 1 7-9 September 2014 7 September Sunday 18:00 Group D 7 September Sunday 20:45 Group D 7 September Sunday 20:45 Group D 7 September Sunday 18:00 Group F 7 September Sunday 20:45 Group F 7 September
Advanced analytics at your hands
2.3 Advanced analytics at your hands Neural Designer is the most powerful predictive analytics software. It uses innovative neural networks techniques to provide data scientists with results in a way previously
A Professional Big Data Master s Program to train Computational Specialists
A Professional Big Data Master s Program to train Computational Specialists Anoop Sarkar, Fred Popowich, Alexandra Fedorova! School of Computing Science! Education for Employable Graduates: Critical Questions
NIH Commons Overview, Framework & Pilots - Version 1. The NIH Commons
The NIH Commons Summary The Commons is a shared virtual space where scientists can work with the digital objects of biomedical research, i.e. it is a system that will allow investigators to find, manage,
How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning
How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume
Welcome. to the Unemployment Benefits Services Tutorial
Welcome to the Unemployment Benefits Services Tutorial Tutorial Overview Checking your Claim and Payment status over the Internet is fast, easy, and secure. The internet is available 24 hours a day, seven
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be
CPSC 340: Machine Learning and Data Mining. Mark Schmidt University of British Columbia Fall 2015
CPSC 340: Machine Learning and Data Mining Mark Schmidt University of British Columbia Fall 2015 Outline 1) Intro to Machine Learning and Data Mining: Big data phenomenon and types of data. Definitions
Visualizing Large, Complex Data
Visualizing Large, Complex Data Outline Visualizing Large Scientific Simulation Data Importance-driven visualization Multidimensional filtering Visualizing Large Networks A layout method Filtering methods
How To Improve Your Profit With Optimized Prediction
Higher Business ROI with Optimized Prediction Yottamine s Unique and Powerful Solution Forward thinking businesses are starting to use predictive analytics to predict which future business events will
An Introduction to Data Mining
An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail
MACHINE LEARNING IN HIGH ENERGY PHYSICS
MACHINE LEARNING IN HIGH ENERGY PHYSICS LECTURE #1 Alex Rogozhnikov, 2015 INTRO NOTES 4 days two lectures, two practice seminars every day this is introductory track to machine learning kaggle competition!
Intrusion Detection via Machine Learning for SCADA System Protection
Intrusion Detection via Machine Learning for SCADA System Protection S.L.P. Yasakethu Department of Computing, University of Surrey, Guildford, GU2 7XH, UK. s.l.yasakethu@surrey.ac.uk J. Jiang Department
Machine Learning in Python with scikit-learn. O Reilly Webcast Aug. 2014
Machine Learning in Python with scikit-learn O Reilly Webcast Aug. 2014 Outline Machine Learning refresher scikit-learn How the project is structured Some improvements released in 0.15 Ongoing work for
Research on Clustering Analysis of Big Data Yuan Yuanming 1, 2, a, Wu Chanle 1, 2
Advanced Engineering Forum Vols. 6-7 (2012) pp 82-87 Online: 2012-09-26 (2012) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/aef.6-7.82 Research on Clustering Analysis of Big Data
Statistics for BIG data
Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before
How In-Memory Data Grids Can Analyze Fast-Changing Data in Real Time
SCALEOUT SOFTWARE How In-Memory Data Grids Can Analyze Fast-Changing Data in Real Time by Dr. William Bain and Dr. Mikhail Sobolev, ScaleOut Software, Inc. 2012 ScaleOut Software, Inc. 12/27/2012 T wenty-first
Predictive Analytics Techniques: What to Use For Your Big Data. March 26, 2014 Fern Halper, PhD
Predictive Analytics Techniques: What to Use For Your Big Data March 26, 2014 Fern Halper, PhD Presenter Proven Performance Since 1995 TDWI helps business and IT professionals gain insight about data warehousing,
ICSES Journal on Image Processing and Pattern Recognition (IJIPPR), Aug. 2015, Vol. 1, No. 1
2 ICSES Journal on Image Processing and Pattern Recognition (IJIPPR), Aug. 2015, Vol. 1, No. 1 1. About ICSES Journal on Image Processing and Pattern Recognition (IJIPPR) The ICSES Journal on Image Processing
Azure Machine Learning, SQL Data Mining and R
Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:
Welcome. Data Mining: Updates in Technologies. Xindong Wu. Colorado School of Mines Golden, Colorado 80401, USA
Welcome Xindong Wu Data Mining: Updates in Technologies Dept of Math and Computer Science Colorado School of Mines Golden, Colorado 80401, USA Email: xwu@ mines.edu Home Page: http://kais.mines.edu/~xwu/
KEITH LEHNERT AND ERIC FRIEDRICH
MACHINE LEARNING CLASSIFICATION OF MALICIOUS NETWORK TRAFFIC KEITH LEHNERT AND ERIC FRIEDRICH 1. Introduction 1.1. Intrusion Detection Systems. In our society, information systems are everywhere. They
COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments
Contents List of Figures Foreword Preface xxv xxiii xv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for
EUROTECH UNIVERSITIES ALLIANCE CONTRIBUTION TO THE PUBLIC CONSULTATION SCIENCE 2.0-SCIENCE IN TRANSITION
EUROTECH UNIVERSITIES ALLIANCE CONTRIBUTION TO THE PUBLIC CONSULTATION SCIENCE 2.0-SCIENCE IN TRANSITION A: INTRODUCTION TO THE ALLIANCE S CONTRIBUTION The EuroTech Universities Alliance is a strategic
Full-text Search in Intermediate Data Storage of FCART
Full-text Search in Intermediate Data Storage of FCART Alexey Neznanov, Andrey Parinov National Research University Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow, 101000, Russia ANeznanov@hse.ru,
Fast Multipole Method for particle interactions: an open source parallel library component
Fast Multipole Method for particle interactions: an open source parallel library component F. A. Cruz 1,M.G.Knepley 2,andL.A.Barba 1 1 Department of Mathematics, University of Bristol, University Walk,
Big Data аs а Business Tool. A two-day crash course for top managers in banking and the payments industry
Big Data as a Business Tool A two-day crash course for top managers in banking and the payments industry 1 What, Why, for Whom? Course goal To provide top managers from banking and the payments industry
The? Data: Introduction and Future
The? Data: Introduction and Future Husnu Sensoy Global Maksimum Data & Information Technologies Global Maksimum Data & Information Technologies The Data Company Massive Data Unstructured Data Insight Information
MS1b Statistical Data Mining
MS1b Statistical Data Mining Yee Whye Teh Department of Statistics Oxford http://www.stats.ox.ac.uk/~teh/datamining.html Outline Administrivia and Introduction Course Structure Syllabus Introduction to
Machine Learning for Big Data Texts, Signals, Images and Video
Sponsored by MIT in collaboration with Skoltech Machine Learning for Big Data Texts, Signals, Images and Video Professor Konstantin Vorontsov Moscow Institute of Physics and Technology December 15, 2014
COMP9321 Web Application Engineering
COMP9321 Web Application Engineering Semester 2, 2015 Dr. Amin Beheshti Service Oriented Computing Group, CSE, UNSW Australia Week 11 (Part II) http://webapps.cse.unsw.edu.au/webcms2/course/index.php?cid=2411
Pavel Makin. Principal supervisor: Aleksey Senkov. UNIS supervisor: Aleksey Marchenko.
Pavel Makin Master student of Saint-Petersburg state marine technical university; department of electrical energetics and automation technology. Bachelor student at UNIS. Principal supervisor: Aleksey
Distributed Computing and Big Data: Hadoop and MapReduce
Distributed Computing and Big Data: Hadoop and MapReduce Bill Keenan, Director Terry Heinze, Architect Thomson Reuters Research & Development Agenda R&D Overview Hadoop and MapReduce Overview Use Case:
How To Create A Text Classification System For Spam Filtering
Term Discrimination Based Robust Text Classification with Application to Email Spam Filtering PhD Thesis Khurum Nazir Junejo 2004-03-0018 Advisor: Dr. Asim Karim Department of Computer Science Syed Babar
Computing Model for SuperBelle
Computing Model for SuperBelle Outline Scale and Motivation Definitions of Computing Model Interplay between Analysis Model and Computing Model Options for the Computing Model Strategy to choose the Model
Advanced In-Database Analytics
Advanced In-Database Analytics Tallinn, Sept. 25th, 2012 Mikko-Pekka Bertling, BDM Greenplum EMEA 1 That sounds complicated? 2 Who can tell me how best to solve this 3 What are the main mathematical functions??
Knowledge Discovery from patents using KMX Text Analytics
Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs anton.heijs@treparel.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchers
The Big Data mining to improve medical diagnostics quality
The Big Data mining to improve medical diagnostics quality Ilyasova N.Yu., Kupriyanov A.V. Samara State Aerospace University, Image Processing Systems Institute, Russian Academy of Sciences Abstract. The
Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence
Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a Support
COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK DEPARTMENT OF INDUSTRIAL ENGINEERING AND OPERATIONS RESEARCH
Course: IEOR 4575 Business Analytics for Operations Research Lectures MW 2:40-3:55PM Instructor Prof. Guillermo Gallego Office Hours Tuesdays: 3-4pm Office: CEPSR 822 (8 th floor) Textbooks and Learning
International University of Monaco 27/04/2012 14:55 - Page 1. Monday 30/04 Tuesday 01/05 Wednesday 02/05 Thursday 03/05 Friday 04/05 Saturday 05/05
International University of Monaco 27/04/12 14:55 - Page 1 Master in International Business and Global Affairs - from 30 avril to 05 mai 12 Monday 30/04 Tuesday 01/05 Wednesday 02/05 Thursday 03/05 Friday
How To Develop Software
Software Development Basics Dr. Axel Kohlmeyer Associate Dean for Scientific Computing College of Science and Technology Temple University, Philadelphia http://sites.google.com/site/akohlmey/ a.kohlmeyer@temple.edu
International University of Monaco 21/05/2012 16:01 - Page 1. Monday 30/04 Tuesday 01/05 Wednesday 02/05 Thursday 03/05 Friday 04/05 Saturday 05/05
International University of Monaco 21/05/12 16:01 - Page 1 Master in International Sport Business and Management - from 30 avril to 05 mai 12 Monday 30/04 Tuesday 01/05 Wednesday 02/05 Thursday 03/05 Friday
Invited Applications Paper
Invited Applications Paper - - Thore Graepel Joaquin Quiñonero Candela Thomas Borchert Ralf Herbrich Microsoft Research Ltd., 7 J J Thomson Avenue, Cambridge CB3 0FB, UK THOREG@MICROSOFT.COM JOAQUINC@MICROSOFT.COM
Feature Subset Selection in E-mail Spam Detection
Feature Subset Selection in E-mail Spam Detection Amir Rajabi Behjat, Universiti Technology MARA, Malaysia IT Security for the Next Generation Asia Pacific & MEA Cup, Hong Kong 14-16 March, 2012 Feature
Machine Learning and Data Mining. Fundamentals, robotics, recognition
Machine Learning and Data Mining Fundamentals, robotics, recognition Machine Learning, Data Mining, Knowledge Discovery in Data Bases Their mutual relations Data Mining, Knowledge Discovery in Databases,
Big-data Analytics: Challenges and Opportunities
Big-data Analytics: Challenges and Opportunities Chih-Jen Lin Department of Computer Science National Taiwan University Talk at 台 灣 資 料 科 學 愛 好 者 年 會, August 30, 2014 Chih-Jen Lin (National Taiwan Univ.)
Fortgeschrittene Computerintensive Methoden
Fortgeschrittene Computerintensive Methoden Einheit 3: mlr - Machine Learning in R Bernd Bischl Matthias Schmid, Manuel Eugster, Bettina Grün, Friedrich Leisch Institut für Statistik LMU München SoSe 2014
Sunnie Chung. Cleveland State University
Sunnie Chung Cleveland State University Data Scientist Big Data Processing Data Mining 2 INTERSECT of Computer Scientists and Statisticians with Knowledge of Data Mining AND Big data Processing Skills:
Manufacturing Execution Systems
Siemens Industrial Automation / October 2013 Manufacturing Execution Systems SIMATIC IT Solution Partner Program siemens.com/answers Mission The role of industrial IT and industry software is becoming
CENG 734 Advanced Topics in Bioinformatics
CENG 734 Advanced Topics in Bioinformatics Week 9 Text Mining for Bioinformatics: BioCreative II.5 Fall 2010-2011 Quiz #7 1. Draw the decompressed graph for the following graph summary 2. Describe the
New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Introduction
Introduction New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Predictive analytics encompasses the body of statistical knowledge supporting the analysis of massive data sets.
Statistics, Data Mining and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data. and Alex Gray
Statistics, Data Mining and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas University of Washington and Alex
References. Introduction to Database Systems CSE 444. Motivation. Basic Features. Outline: Database in the Cloud. Outline
References Introduction to Database Systems CSE 444 Lecture 24: Databases as a Service YongChul Kwon Amazon SimpleDB Website Part of the Amazon Web services Google App Engine Datastore Website Part of
Introduction to Database Systems CSE 444
Introduction to Database Systems CSE 444 Lecture 24: Databases as a Service YongChul Kwon References Amazon SimpleDB Website Part of the Amazon Web services Google App Engine Datastore Website Part of
Machine Learning. 01 - Introduction
Machine Learning 01 - Introduction Machine learning course One lecture (Wednesday, 9:30, 346) and one exercise (Monday, 17:15, 203). Oral exam, 20 minutes, 5 credit points. Some basic mathematical knowledge
Analyzing Big Data with AWS
Analyzing Big Data with AWS Peter Sirota, General Manager, Amazon Elastic MapReduce @petersirota What is Big Data? Computer generated data Application server logs (web sites, games) Sensor data (weather,
Data Mining Methods: Applications for Institutional Research
Data Mining Methods: Applications for Institutional Research Nora Galambos, PhD Office of Institutional Research, Planning & Effectiveness Stony Brook University NEAIR Annual Conference Philadelphia 2014
MACHINE LEARNING. Meetup Tutorial - 22 January 2015 LADISPE - Politecnico di Torino - Italy. bit.ly/ml-italy
MACHINE LEARNING Meetup Tutorial - 22 January 2015 LADISPE - Politecnico di Torino - Italy bit.ly/ml-italy Why we are committed in growing a local MACHINE LEARNING and DEEP LEARNING community? Because
CSCI6900 Assignment 2: Naïve Bayes on Hadoop
DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF GEORGIA CSCI6900 Assignment 2: Naïve Bayes on Hadoop DUE: Friday, September 18 by 11:59:59pm Out September 4, 2015 1 IMPORTANT NOTES You are expected to use
Scalable Machine Learning to Exploit Big Data for Knowledge Discovery
Scalable Machine Learning to Exploit Big Data for Knowledge Discovery Una-May O Reilly MIT MIT ILP-EPOCH Taiwan Symposium Big Data: Technologies and Applications Lots of Data Everywhere Knowledge Mining
Machine Learning What, how, why?
Machine Learning What, how, why? Rémi Emonet (@remiemonet) 2015-09-30 Web En Vert $ whoami $ whoami Software Engineer Researcher: machine learning, computer vision Teacher: web technologies, computing
Data Mining and Exploration. Data Mining and Exploration: Introduction. Relationships between courses. Overview. Course Introduction
Data Mining and Exploration Data Mining and Exploration: Introduction Amos Storkey, School of Informatics January 10, 2006 http://www.inf.ed.ac.uk/teaching/courses/dme/ Course Introduction Welcome Administration
MACHINE LEARNING BASICS WITH R
MACHINE LEARNING [Hands-on Introduction of Supervised Machine Learning Methods] DURATION 2 DAY The field of machine learning is concerned with the question of how to construct computer programs that automatically
Chapter 6. The stacking ensemble approach
82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described
Fall 2012 Q530. Programming for Cognitive Science
Fall 2012 Q530 Programming for Cognitive Science Aimed at little or no programming experience. Improve your confidence and skills at: Writing code. Reading code. Understand the abilities and limitations
In this presentation, you will be introduced to data mining and the relationship with meaningful use.
In this presentation, you will be introduced to data mining and the relationship with meaningful use. Data mining refers to the art and science of intelligent data analysis. It is the application of machine
Data Mining: STATISTICA
Data Mining: STATISTICA Outline Prepare the data Classification and regression 1 Prepare the Data Statistica can read from Excel,.txt and many other types of files Compared with WEKA, Statistica is much
Journée Thématique Big Data 13/03/2015
Journée Thématique Big Data 13/03/2015 1 Agenda About Flaminem What Do We Want To Predict? What Is The Machine Learning Theory Behind It? How Does It Work In Practice? What Is Happening When Data Gets
Academic Calendar for Faculty
Summer 2013 Term June 3, 2013 (Monday) June 3-4, 2013 (Monday Tuesday) June 5, 2013 (Wednesday) June 5-6, 2013 (Wednesday Thursday) June 6, 2013 (Thursday) July 3, 2013 (Wednesday) July 4, 2013 (Thursday)
Keywords data mining, prediction techniques, decision making.
Volume 5, Issue 4, April 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analysis of Datamining
Analysis Tools and Libraries for BigData
+ Analysis Tools and Libraries for BigData Lecture 02 Abhijit Bendale + Office Hours 2 n Terry Boult (Waiting to Confirm) n Abhijit Bendale (Tue 2:45 to 4:45 pm). Best if you email me in advance, but I
Car Insurance. Prvák, Tomi, Havri
Car Insurance Prvák, Tomi, Havri Sumo report - expectations Sumo report - reality Bc. Jan Tomášek Deeper look into data set Column approach Reminder What the hell is this competition about??? Attributes
Applying Deep Learning to Car Data Logging (CDL) and Driver Assessor (DA) October 22-Oct-15
Applying Deep Learning to Car Data Logging (CDL) and Driver Assessor (DA) October 22-Oct-15 GENIVI is a registered trademark of the GENIVI Alliance in the USA and other countries Copyright GENIVI Alliance
Big-Data Computing with Smart Clouds and IoT Sensing
A New Book from Wiley Publisher to appear in late 2016 or early 2017 Big-Data Computing with Smart Clouds and IoT Sensing Kai Hwang, University of Southern California, USA Min Chen, Huazhong University
What is DART Automated Phone Access (APA)?
Consumer Tutorial What is DART Automated Phone Access (APA)? An easy to use automated phone system that only supports callers using a telephone touch tone key pad. System does not have voice capability.
BIG DATA What it is and how to use?
BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14
CHARACTERISTICS IN FLIGHT DATA ESTIMATION WITH LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINES
CHARACTERISTICS IN FLIGHT DATA ESTIMATION WITH LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINES Claus Gwiggner, Ecole Polytechnique, LIX, Palaiseau, France Gert Lanckriet, University of Berkeley, EECS,
Car Insurance. Havránek, Pokorný, Tomášek
Car Insurance Havránek, Pokorný, Tomášek Outline Data overview Horizontal approach + Decision tree/forests Vertical (column) approach + Neural networks SVM Data overview Customers Viewed policies Bought
BIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics http://www.ccmb.med.umich.edu/node/1376
Course Director: Dr. Kayvan Najarian (DCM&B, kayvan@umich.edu) Lectures: Labs: Mondays and Wednesdays 9:00 AM -10:30 AM Rm. 2065 Palmer Commons Bldg. Wednesdays 10:30 AM 11:30 AM (alternate weeks) Rm.
Machine Learning. Chapter 18, 21. Some material adopted from notes by Chuck Dyer
Machine Learning Chapter 18, 21 Some material adopted from notes by Chuck Dyer What is learning? Learning denotes changes in a system that... enable a system to do the same task more efficiently the next
How To Understand How Weka Works
More Data Mining with Weka Class 1 Lesson 1 Introduction Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz More Data Mining with Weka a practical course
Big Data: A Closer Look
Big Data: A Closer Look What is Big Data? Increasingly popular search query http://www.google.com/trends/explore#q=big%20data What is Big Data? the V s Characteristics of Big Data Volume http://www.businessweek.com/articles/2014-03-06/interactive-graphichow-big-is-big-data
Globus Genomics Tutorial GlobusWorld 2014
Globus Genomics Tutorial GlobusWorld 2014 Agenda Overview of Globus Genomics Example Collaborations Demonstration Globus Genomics interface Globus Online integration Scenario 1: Using Globus Genomics for
COMP3420: Advanced Databases and Data Mining. Classification and prediction: Introduction and Decision Tree Induction
COMP3420: Advanced Databases and Data Mining Classification and prediction: Introduction and Decision Tree Induction Lecture outline Classification versus prediction Classification A two step process Supervised
Spring Semester May 2016 Exam Timetable
Spring Semester May 2016 Exam Timetable School of Science & Computing Department of Computing Please ensure that you are in possession of a current ID card. If not you should contact the Registrar's Office
An Integrated CyberSecurity Approach for HEP Grids. Workshop Report. http://hpcrd.lbl.gov/hepcybersecurity/
An Integrated CyberSecurity Approach for HEP Grids Workshop Report http://hpcrd.lbl.gov/hepcybersecurity/ 1. Introduction The CMS and ATLAS experiments at the Large Hadron Collider (LHC) being built at
Role of Cloud Computing in Big Data Analytics Using MapReduce Component of Hadoop
Role of Cloud Computing in Big Data Analytics Using MapReduce Component of Hadoop Kanchan A. Khedikar Department of Computer Science & Engineering Walchand Institute of Technoloy, Solapur, Maharashtra,
Limitations of Human Vision. What is computer vision? What is computer vision (cont d)?
What is computer vision? Limitations of Human Vision Slide 1 Computer vision (image understanding) is a discipline that studies how to reconstruct, interpret and understand a 3D scene from its 2D images
Building a Volunteer Cloud
Building a Volunteer Cloud Ben Segal, Predrag Buncic, David Garcia Quintas / CERN Daniel Lombrana Gonzalez / University of Extremadura Artem Harutyunyan / Yerevan Physics Institute Jarno Rantala / Tampere
Predicting the Risk of Heart Attacks using Neural Network and Decision Tree
Predicting the Risk of Heart Attacks using Neural Network and Decision Tree S.Florence 1, N.G.Bhuvaneswari Amma 2, G.Annapoorani 3, K.Malathi 4 PG Scholar, Indian Institute of Information Technology, Srirangam,
Russian MegaProject. ATLAS SW&C week Plenary session : status, problems and plans. Feb 24, 2014. Alexei Klimentov Brookhaven National Laboratory
Russian MegaProject ATLAS SW&C week Plenary session : status, problems and plans Feb 24, 2014 Alexei Klimentov Brookhaven National Laboratory Overview Russian Federation Government grants Big Data Technologies
Challenges for Data Driven Systems
Challenges for Data Driven Systems Eiko Yoneki University of Cambridge Computer Laboratory Quick History of Data Management 4000 B C Manual recording From tablets to papyrus to paper A. Payberah 2014 2
TRAINING STUDENTS TO EXTRACT VALUE FROM BIG DATA
TRAINING STUDENTS TO EXTRACT VALUE FROM BIG DATA A National Research Council Workshop Sponsored by the National Science Foundation COMMITTEE ON APPLIED AND THEORETICAL STATISTICS APRIL 11-12, 2014 500
Cloud Sobriety. Technical challenges in mapping Informatics to the cloud. Chris Dagdigian 2010 NHGRI Cloud Workshop
Cloud Sobriety Technical challenges in mapping Informatics to the cloud Chris Dagdigian 2010 NHGRI Cloud Workshop Welcome To Day 2! Excellent talk lineup for today! Focus on implementation, architecture