1 Specialized Machine Learning Topics Lantz Ch 12 Wk 6, Part 2 Above – Specialized bicycle – a tandem track bike. Note that the seats are not adjustable,

Slides:



Advertisements
Similar presentations
PRE-SCHOOL QUANT WORKSHOP II R THROUGH EXCEL. NEW YORK TIMES INFOGRAPHICS GALARY The Jobless Rate for People Like You Home Prices in Selected Cities For.
Advertisements

Nokia Technology Institute Natural Partner for Innovation.
Distributed Graph Analytics Imranul Hoque CS525 Spring 2013.
Running Hadoop-as-a-Service in the Cloud
Sarah Reonomy OSCON 2014 ANALYZING DATA WITH PYTHON.
Two Broad Categories of Software
Big Data Analytics Module 4 – Data Mining and Predictive Analytics Including Mahout Saptak Sen, Microsoft Bill Ramos, Advaiya.
Introduction.  What is the 3D graphics.  Applications of 3D Graphics.  What is 3Ds MAX.?  System requirements for 3Ds max.  Components of 3Ds MAX.
SYSTEMS SUPPORT FOR GRAPHICAL LEARNING Ken Birman 1 CS6410 Fall /18/2014.
Bioinformatics Protein structure prediction Motif finding Clustering techniques in bioinformatics Sequence alignment and comparison Phylogeny Applying.
Introduction of Apache Hama Edward J. Yoon, October 11, 2011.
Design Patterns for Efficient Graph Algorithms in MapReduce Jimmy Lin and Michael Schatz University of Maryland MLG, January, 2014 Jaehwan Lee.
Tyson Condie.
The Confident Writer Chapter 9: Explaining a Process.
SUPPORTING A MODELING CONTINUUM IN SCALATION John A. Miller Michael E. Cotterell Stephen J. Buckley University of Georgia IBM Thomas J. Watson Research.
COMPUTER SOFTWARE Section 2 “System Software: Computer System Management ” CHAPTER 4 Lecture-6/ T. Nouf Almujally 1.
Computer Parts When you build your own computer, choose from these parts…
CS525: Big Data Analytics Machine Learning on Hadoop Fall 2013 Elke A. Rundensteiner 1.
© What do bioinformaticians do?
Location-aware MapReduce in Virtual Cloud 2011 IEEE computer society International Conference on Parallel Processing Yifeng Geng1,2, Shimin Chen3, YongWei.
SYSTEMS SUPPORT FOR GRAPHICAL LEARNING Ken Birman 1 CS6410 Fall /18/2014.
UNIT - 1Topic - 2 C OMPUTING E NVIRONMENTS. What is Computing Environment? Computing Environment explains how a collection of computers will process and.
© 2007 Pearson Addison-Wesley. All rights reserved 0-1 Spring(2007) Instructor: Qiong Cheng © 2007 Pearson Addison-Wesley. All rights reserved.
COMP 2903 A34s – Google and the Wisdom of Clouds Danny Silver JSOCS, Acadia University.
Face Detection And Recognition For Distributed Systems Meng Lin and Ermin Hodžić 1.
Other formats for data Linked lists, Hash tables, JSON, Big Data, Hadoop & MapReduce. REST. Parallel processing exercise Homework: Plans for group sorting.
1 Intern Project Presentation Connor Richardson Big Data August 4, 2015.
Oracle’s Big Plans For Big Data Analysis By Doug Henschen, InformationWeek, Oct 4 th, 2011 Presented by Group 7: Sam Tucker and Ayoung Noh.
Performance Evaluation of Image Conversion Module Based on MapReduce for Transcoding and Transmoding in SMCCSE Speaker : 吳靖緯 MA0G IEEE.
Business computing Session 7 Excel, Links, Graphics, Goal seeking.
What’s the Big Deal About R? Tom Tiedeman, OCIO July 21, 2015.
McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 4 Computer Software.
Old Bikes Verses Drag Bikes. Old Verses New The first motorcycle was made in It had a wood frame with a metal coating. The wheels were wagon wheels.
1. Speed, Velocity, & Acceleration 2 You know a car is in motion if you see it in one place 3 then in another place in relation to an object.
Big data Usman Roshan CS 675. Big data Typically refers to datasets with very large number of instances (rows) as opposed to attributes (columns). Data.
Technology Education THE PERSONAL COMPUTER (PC) HARDWARE PART 1.
COMPUTER HARDWARE AND SOFTWARE Technology Competency.
Social Media and Networking: Academic Kristina Lerman USC Information Sciences Institute
PARALLEL AND DISTRIBUTED PROGRAMMING MODELS U. Jhashuva 1 Asst. Prof Dept. of CSE om.
Data Mining in Germany IIM Conference, Oct. 24, 2012 Gottfried Schwarz, DLR > Lecture > Author Document > Datewww.DLR.de Chart 1.
Copyright © 2016 Pearson Education, Inc. Modern Database Management 12 th Edition Jeff Hoffer, Ramesh Venkataraman, Heikki Topi CHAPTER 11: BIG DATA AND.
Part III BigData Analysis Tools (Storm) Yuan Xue
BIG DATA BIGDATA, collection of large and complex data sets difficult to process using on-hand database tools.
Raju Subba Open Source Project: Apache Spark. Introduction Big Data Analytics Engine and it is open source Spark provides APIs in Scala, Java, Python.
Accelerated B.S./M.S An approved Accelerated BS/MS program allows an undergraduate student to take up to 6 graduate level credits as an undergraduate.
Big data toolbox.
Fig. 2. In adaptive permutation, the pool of candidate SNPs decreases as p-value estimates become more precise. Running time increases with the number.
COMP9313: Big Data Management Lecturer: Xin Cao Course web site:
An Overview of the Computer System
Introduction to Spark Streaming for Real Time data analysis
Sections Text Mining Plan Twitter API twitteR package
Spark Presentation.
Financial calculators on Web
DATA SCIENCE Online Training at GoLogica
Computational Thinking
Chapter 4 Computer Software.
ECO FRIENDLY TRANSPORTION SYSTEM
Looking Inside the machine (Types of hardware, CPU, Memory)
An Overview of the Computer System
Network Visualization
湖南大学-信息科学与工程学院-计算机与科学系
COS 518: Advanced Computer Systems Lecture 12 Mike Freedman
Martin Swany Gregor von Laszewski Thomas Sterling Clint Whaley
Sky Computing on FutureGrid and Grid’5000
Parallel Applications And Tools For Cloud Computing Environments
Overview of big data tools
Spark and Scala.
Thales Alenia Space Competence Center Software Solutions
Big-Data Analytics with Azure HDInsight
Sky Computing on FutureGrid and Grid’5000
Presentation transcript:

1 Specialized Machine Learning Topics Lantz Ch 12 Wk 6, Part 2 Above – Specialized bicycle – a tandem track bike. Note that the seats are not adjustable, and there are no brakes.

2 All the things you may need to know – sometime! There are packages to convert various kinds of data in and out of R, like: – Web scraping – XML – JSON – Excel – Bioinformatics – Social network data – Graph data

3 And there are performance tools Making dataframes faster for subsetting, joining, and grouping Creating disk-based dataframes Using massive matrices Using parallel computing and multitasking Cloud computing with MapReduce and Hadoop Run R on your graphics card! Build big regression models and random forests