Home | Skytree – Machine Learning on Big Data for Predictive Analytics Mendel HMM Toolbox for Matlab Written by Steinar Thorvaldsen, 2004. Last updated: Jan. 2006. Dept. of Mathematics and Statistics University of Tromsø - Norway. steinart@math.uit.no MendelHMM is a Hidden Markov Model (HMM) tutorial toolbox for Matlab. To run the program you should make the following steps: 1. When you type "mendelHMM" in Matlab command window the main window of GUI will appear. Main window of the program. In his historic experiment, Gregor Mendel (1822-1884) examined 7 simple traits in the common garden pea (Pisum). Today we know that the recessive expressions most often are mutations in the DNA molecule of the gene, as it is well known for Mendel’s growth gene (trait 7) where a single nucleotide G is substituted with an A. In his experiment Mendel also studied in more detail the plant seeds with two and three heredity factors simultaneously. The estimate of a statistical model according to a training set There are two main types of learning. The sampling of new training data y = (A, A, a, a, a) 1. 2. 3.
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 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 A list of important user-visible changes is availble at by selecting the Release Notes item in the News menu of the GUI, or by typing news at the Octave command prompt. Thanks to the many people who contributed to this release!
DataGravity | Changing the game in data storage General Hidden Markov Model Library | Free Science & Engineering software downloads 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. It is also well-suited for developing new machine learning schemes. 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.
GoodData | Experience SaaS Business Intelligence Online Code Repository The goal is to have working code for all the algorithms in the book in a variety of languages. So far, we have Java, Lisp and Python versions of most of the algorithms. There is also some old code in C++, C# and Prolog, but these are not being maintained. We also have a directory full of data files. Let peter@norvig.com know what languages you'd like to see, and if you're willing to help. Supported Implementations We offer the following three language choices, plus a selection of data that works with all the implementations: Java: aima-java project, by Ravi Mohan. Unsupported Implementations Implementation Choices What languages are instructors recommending? Of course, neither recall nor precision is perfect for these queries, nor is the estimated number of results guaranteed to be accurate, but they offer a rough estimate of popularity.
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. In practice, most of the data mining literature is too abstract regarding the actual use of the algorithms and parameter tuning is usually a frustrating task. On the other hand, there is a large number of implementations available, such as those in the R project, but their documentation focus mainly on implementation details without providing a good discussion about parameter-related trade-offs associated with each of them.
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