A Short Introduction to Probability and Related
Concepts
Harald Goldstein
konomisk institutt
August 2003
Contents
1 Introduction
2
1
Events and Probability
2.1 Mathematical description of events . . .
2.2 Probability . . . . . . . . . . . . . . . .
2.3 Mo
CHAPTER 1
Introduction
Congratulations! Youve just begun your quest to become an R programmer. So you
dont pull any mental muscles, this chapter starts you off gently with a nice warm-up.
Before you begin coding, were going to talk about what R is, and ho
IJCEM International Journal of Computational Engineering & Management, Vol. 17 Issue 5, September 2014
ISSN (Online): 2230-7893
www.IJCEM.org
9
Big Data Analysis using R and Hadoop
Anju Gahlawat
Tata Consultancy Services Ltd.
4 & 5 Floor, PTI Bldg, 4, Par
Case Study 4: Collaborative Filtering
Collaborative Filtering
Matrix Completion
Alternating Least Squares
Machine Learning/Statistics for Big Data
CSE599C1/STAT592, University of Washington
Carlos Guestrin
February 28th, 2013
Carlos Guestrin 2013
1
Collab
Case Study 1: Estimating Click Probabilities
Tackling an Unknown
Number of Features with
Sketching
Machine Learning/Statistics for Big Data
CSE599C1/STAT592, University of Washington
Carlos Guestrin
January 22nd, 2013
1
Carlos Guestrin 2013
Sketching Coun
Case Study 2: Document Retrieval
Finding Similar Documents
Using Nearest Neighbors
Machine Learning/Statistics for Big Data
CSE599C1/STAT592, University of Washington
Emily Fox
January 22nd, 2013
Emily Fox 2013
1
Nearest Neighbor with KD Trees
n
Using the
Case Study 2: Document Retrieval
Finding Similar Documents
Using Nearest Neighbors
Machine Learning/Statistics for Big Data
CSE599C1/STAT592, University of Washington
Emily Fox
January 22nd, 2013
Emily Fox 2013
1
Nearest Neighbor with KD Trees
n
Using the
Case Study 1: Estimating Click Probabilities
L2 Regularization for
Logistic Regression
Machine Learning/Statistics for Big Data
CSE599C1/STAT592, University of Washington
Carlos Guestrin
January 10th, 2013
1
Carlos Guestrin 2013
Logistic Regression
n
Logi
Case Study 1: Estimating Click Probabilities
Stochastic Gradient
Descent (continued)
Machine Learning/Statistics for Big Data
CSE599C1/STAT592, University of Washington
Carlos Guestrin
January 17th, 2013
Carlos Guestrin 2013
1
What is the Perceptron Doing
University of Washington
Department of Computer Science and Engineering / Department of Statistics
CSE 599 / Stat 592 Machine Learning (Statistics) for Big Data
Homework 4
Winter 2013
Issued: Sunday, March 3, 2013
Due: Tuesday,March 12, 2013
Suggested Rea
University of Washington
Department of Computer Science and Engineering / Department of Statistics
CSE 599 / Stat 592 Machine Learning (Statistics) for Big Data
Homework 3 Midterm
Winter 2013
Must be done individually, without any communication with
other
University of Washington
Department of Computer Science and Engineering / Department of Statistics
CSE 599 / Stat 592 Machine Learning (Statistics) for Big Data
Homework 2
Winter 2013
Issued: Thursday, January 31, 2013
Due: Thursday, February 14, 2013
Sug
University of Washington
Department of Computer Science and Engineering / Department of Statistics
CSE 599 / Stat 592 Machine Learning (Statistics) for Big Data
Homework 1
Winter 2013
Issued: Tuesday, January 15, 2013
Due: Tuesday, January 29, 2013
Sugges
Machine Learning for Big Data (CSE 599)
Sta8s8cs for Big Data (STAT 592)
(Or how to do really kickass research
in the age of big data)
Course Sta
Instructors:
Emily Fox (Stat)
Carlos Guestrin (CSE)
T
Case Study 1: Estimating Click Probabilities
Tackling an Unknown
Number of Features with
Sketching
Machine Learning/Statistics for Big Data
CSE599C1/STAT592, University of Washington
Carlos Guestrin
January 22nd, 2013
Carlos Guestrin 2013
1
Sketching Coun
Comparing NoSQL MongoDB to an SQL DB
Zachary Parker
Scott Poe
Susan V. Vrbsky
The University of Alabama
Center for Advanced Public Safety
Tuscaloosa, AL 35487-0290
(205) 348-6363
The University of Alabama
Center for Advanced Public Safety
Tuscaloosa, AL 3
Scalable SQL and NoSQL Data Stores
Rick Cattell
Originally published in 2010, last
revised December 2011
ABSTRACT
In this paper, we examine a number of SQL and socalled NoSQL data stores designed to scale simple
OLTP-style application loads over many serv
buyers Guide
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Time to rethink
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editors
comment
Opinion
B
Hammes, Medero, et al.
NoSQL and SQL Databases
Comparison of NoSQL and SQL Databases in the Cloud
Dayne Hammes
Georgia Southern University
[email protected]
Hiram Medero
Georgia Southern University
[email protected]
Harrison Mitch
BUYERS GUIDE TO BIG DATA APPLIANCES | PART 2 OF 3
FIND THE RIGHT BIG DATA
APPLIANCE FOR YOUR BUSINESS
SORBETTO/ISTOCK
With the world of big data analysis still at a relatively immature level,
a big data appliance should be chosen carefully to ensure its v
Scalable SQL and NoSQL Data Stores
Rick Cattell
Cattell.Net Software
Email: [email protected]
ABSTRACT
In this paper, we examine a number of SQL and socalled NoSQL data stores designed to scale simple
OLTP-style application loads over many servers.
Origina
International Journal of Applied Information Systems (IJAIS) ISSN : 2249-0868
Foundation of Computer Science FCS, New York, USA
Volume 5 No.4, March 2013 www.ijais.org
Type of NOSQL Databases and its Comparison with
Relational Databases
Ameya Nayak
Anil P
See discussions, stats, and author profiles for this publication at: https:/www.researchgate.net/publication/258328266
Database Management Systems: A NoSQL
Analysis
Article September 2013
CITATIONS
READS
3
1,125
2 authors:
Innocent Mapanga
Prudence Kadebu
Informatica Economic vol. 17, no. 2/2013
41
NoSQL and SQL Databases for Mobile Applications.
Case Study: MongoDB versus PostgreSQL
Marin FOTACHE, Dragos COGEAN
Al. I. Cuza University of Iasi, Romania
[email protected], [email protected]
Compared with
Case Study 1: Estimating Click Probabilities
L2 Regularization for
Logistic Regression
Machine Learning/Statistics for Big Data
CSE599C1/STAT592, University of Washington
Carlos Guestrin
January 10th, 2013
1
Carlos Guestrin 2013
Logistic Regression
n
Logi
Case Study 1: Estimating Click Probabilities
Perceptron Algorithm
Kernels (continued)
Machine Learning/Statistics for Big Data
CSE599C1/STAT592, University of Washington
Carlos Guestrin
January 15th, 2013
Carlos Guestrin 2013
1
Online Learning Problem
n
A
Case Study 1: Estimating Click Probabilities
Intro
Logistic Regression
Gradient Descent
Machine Learning/Statistics for Big Data
CSE599C1/STAT592, University of Washington
Carlos Guestrin
January 8th, 2013
Carlos Guestrin 2013
1
Ad Placement Strategies
n
Regression and
Machine Learning
Bianca Cung
Justin Hsueh
Levon Kolesnikov
Khang Lu
Least Squares
Linear Regression
An Introduction to Regression
What is Regression?
Type of Data Mining
Recall
from Lecture 21: Data Mining is the
analysis of large amounts
Detection of Outliers
http:/www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
Introduction
An outlier is an observation that appears to deviate
markedly from other observations in the sample.
Identification of potential outliers is important for th
CSE 599d - Quantum Computing The Quantum Fourier Transform and Jordan's Algorithm
Dave Bacon
Department of Computer Science & Engineering, University of Washington
After Simon's algorithm, the next big breakthrough in quantum algorithms occurred when Pete