Weka---Machine Learning Software in Java | Free software downloads Home | Skytree – Machine Learning on Big Data for Predictive Analytics Octave GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The Octave language is quite similar to Matlab so that most programs are easily portable. Octave is distributed under the terms of the GNU General Public License. Version 4.0.0 has been released and is now available for download. An official Windows binary installer is also available from Thanks to the many people who contributed to this release!
Weka 3 - Data Mining with Open Source Machine Learning Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. Weka is open source software issued under the GNU General Public License. Yes, it is possible to apply Weka to big data! Data Mining with Weka is a 5 week MOOC, which was held first in late 2013.
DataGravity | Changing the game in data storage Data Mining Algorithms In R In general terms, Data Mining comprises techniques and algorithms, for determining interesting patterns from large datasets. There are currently hundreds (or even more) algorithms that perform tasks such as frequent pattern mining, clustering, and classification, among others. Understanding how these algorithms work and how to use them effectively is a continuous challenge faced by data mining analysts, researchers, and practitioners, in particular because the algorithm behavior and patterns it provides may change significantly as a function of its parameters. This Wikibook aims to fill this gap by integrating three pieces of information for each technique: description and rationale, implementation details, and use cases. If you want to learn how to program in the R language, read the book R Programming. Contents[edit] External links[edit]
COC131 Data Mining, Tuotorials Weka "The overall goal of our project is to build a state-of-the-art facility for developing machine learning (ML) techniques and to apply them to real-world data mining problems. Our team has incorporated several standard ML techniques into a software "workbench" called WEKA, for Waikato Environment for Knowledge Analysis. With it, a specialist in a particular field is able to use ML to derive useful knowledge from databases that are far too large to be analysed by hand. WEKA's users are ML researchers and industrial scientists, but it is also widely used for teaching." Tutorial 01 (13/02/09) Get the old faithful data-set (.csv) here Get the tutorial 01 exercises here Get the tutorial 01 solutions here Statistics revision for Tutorial 01 here Tutorial 02 (20/02/09) Get the iris data-set (.arff) here Get the tutorial 02 exercises here Tutorial 03 (27/02/09) Get the tutorial 03 exercises here Tutorial 04 (06/03/09) Tutorial 03 exercises and clarification of any issues from earlier tutorials
GoodData | Experience SaaS Business Intelligence Togaware: One Page R: A Survival Guide to Data Science with R Step-by-Step Guide to Setting Up an R-Hadoop System - RDataMining.com: R and Data Mining 1. Set up single-node Hadoop If building a Hadoop system for the first time, you are suggested to start with a stand-alone mode first, and then switch to pseudo-distributed mode and cluster (fully-distributed) mode. 1.1 Download Hadoop Download Hadoop from and then unpack it. 1.2 Set up Hadoop in standalone mode 1.2.1 Set JAVA_HOME In file conf/hadoop_env.sh, add the line below: export JAVA_HOME=/Library/Java/Home 1.2.2 Set up remote desktop and enabling self-login Open the “System Preferences” window, and click “Sharing”“ (under "Internet & Wireless”). After that, save authorized keys so that you can log in localhost without typing a password. ssh-keygen -t rsa -P "" cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys The above step to set up remote desktop and self-login was picked up from which provides detailed instructions to set up Hadoop on Mac. 3.
R Programming/Descriptive Statistics In this section, we present descriptive statistics, ie a set of tools to describe and explore data. This mainly includes univariate and bivariate statistical tools. Generic Functions[edit] We introduce some functions to describe a dataset. names() gives the names of each variablestr() gives the structure of the datasetsummary() gives the mean, median, min, max, 1st and 3rd quartile of each variable in the data.describe() (Hmisc package) gives more details than summary() > library("Hmisc")> describe(mydat) contents() (Hmisc package)dims() in the Zelig package.descr() in the descr package gives min, max, mean and quartiles for continuous variables, frequency tables for factors and length for character vectors.whatis() (YaleToolkit) gives a good description of a dataset.describe() in the psych package also provides summary statistics: Univariate analysis[edit] Continuous variable[edit] Moments[edit] Order statistics[edit] Inequality Index[edit] Concentration index Poverty index Andersen Darling Test :
Заглавная страница Nikolai Yu. Zolotykh pages Home | News | О курсе | UNN Machine Learning Contest | Лабораторные работы | Лекции | Ссылки | Машинное обучение для всех | Минипроекты | Практика | Экзамен и зачет | Tell your friends about this site: | RSS feed: Разработка курса поддержана компанией Intel в 2007. Мои благодарности кураторам: Виктору Ерухимову и Игорю Чикалову. News 11 января 2016Экзамен по машинному обучению состоится 14 января в 317а (2) ауд. 6 января 2016Студенческий контест по Machine Learning от mail.ru! 5 января 2016Вопросы к экзамену 2015 23 декабря 2015 Зачет по машинному обучению (у тех, у кого должен быть зачет) состоится 26 декабря (суббота) в 13:00 в ауд. 217a(II). 11 декабря 2015Презентации к текущим лекциям (осенний семестр 2015) О курсе Ориентировочная программа курса UNN Machine Learning Contest Лабораторные работы Лекции Сообщения об опечатках, ошибках и проч. приветствуются. Ссылки Машинное обучение для всех Глоссарий терминов по машинному обучению (не для математиков!) Минипроекты Журнал Практика Экзамен и зачет
hcistats:start [Koji Yatani's Course Webpage] Disclaimer (Please read this first!) This wiki was initially started as my personal note of statistical methods commonly used in HCI research, but I decided to make it public and put more content in it because I think this may be useful for some of you (particularly if you use R). I will also put some codes for R, so you can quickly apply the methods to your data. This wiki does not emphasize mathematical aspects of statistics much, and rather tries to provide some intuitions of them. Thus, if you know maths, you may be unhappy about this wiki, but this is the way this wiki exists. Keep in mind that I am not an expert of statistics. I also strongly recommend you to get the second opinion on your analysis from other kinds of resource before you really run a test. In this website, I use R to show some examples of how you can run statistical tests. What is this page about? Why R? There are different kinds of statistical software, such as SPSS and SAS. Experimental Design Parametric Tests