Introducing Apache Mahout Scalable, commercial-friendly machine learning for building intelligent applications Grant IngersollPublished on September 08, 2009 Increasingly, the success of companies and individuals in the information age depends on how quickly and efficiently they turn vast amounts of data into actionable information. Whether it's for processing hundreds or thousands of personal e-mail messages a day or divining user intent from petabytes of weblogs, the need for tools that can organize and enhance data has never been greater. Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous experiences. After giving a brief overview of machine-learning concepts, I'll introduce you to the Apache Mahout project's features, history, and goals. Machine learning 101 Machine learning uses run the gamut from game playing to fraud detection to stock-market analysis. Collaborative filteringClusteringCategorization Figure 1.
Self-Repairing Bayesian Inference | StatsBlogs.com (This article was originally published at Normal Deviate, and syndicated at StatsBlogs.) Peter Grunwald gave a talk in the statistics department on Monday. Peter does very interesting work and the material he spoke about is no exception. Here are my recollections from the talk. The summary is this: Peter and John Langford have a very cool example of Bayesian inconsistency, much different than the usual examples of inconsistency. All previous examples of inconsistency in Bayesian inference that I know of have two things in common: the parameter space is complicated and the prior does not put enough mass around the true distribution. Let be a countable parameter space. is wrong. is not in . , the distribution in closest (in Kullback-Leibler distance) to . . . On the other hand, there are papers like Kleijn and van der Vaart (The Annals of Statistics, 2006, pages 837–877) that show that the posterior does indeed concentrate around . is not convex. onto does not equal the projection of . . .
hunch Part I slides (Powerpoint) Introduction Part II.a slides (Powerpoint) Tree Ensembles Part II.b slides (Powerpoint) Graphical models Part III slides (Summary + GPU learning + Terascale linear learning) This tutorial gives a broad view of modern approaches for scaling up machine learning and data mining methods on parallel/distributed platforms. The tutorial is based on (but not limited to) the material from our upcoming Cambridge U. Presenters Ron Bekkerman is a senior research scientist at LinkedIn where he develops machine learning and data mining algorithms to enhance LinkedIn products. Misha Bilenko is a researcher in Machine Learning and Intelligence group at Microsoft Research, which he joined in 2006 after receiving his PhD from the University of Texas at Austin. John Langford is a senior researcher at Yahoo!
Bouygues Telecom improves IPTV intelligence France’s Bouygues Telecom has implemented a multimedia content marketing system from Motorola Mobility to more effectively market its expanding content catalogue. Motorola’s Media Merchandiser solution enables Bouygues to offer subscriber-personalised bundle marketing, and encourages impulse purchases with targeted offers, pricing and discounts. Capable of marketing and delivering content across managed, over-the-top and mobile networks, the solution also features Digital Rights Management (DRM) license issuance to multiple DRM systems. Bouygues delivers triple-play services via its ‘BBox’ gateway, and while recent IPTV subscriber figures are hard to come by, the company had reached just under 600,000 BBox customers by the end of 2010. The telco recently revealed plans to introduce a new high-end version of its BBox gateway this month, called ‘BBox Sensation’ – full details here. We welcome reader discussion and request that you please comment using an authentic name.
Learning From Data MOOC - The Lectures Taught by Feynman Prize winner Professor Yaser Abu-Mostafa. The fundamental concepts and techniques are explained in detail. The focus of the lectures is real understanding, not just "knowing." Lectures use incremental viewgraphs (2853 in total) to simulate the pace of blackboard teaching. The 18 lectures (below) are available on different platforms: Here is the playlist on YouTube Lectures are available on iTunes U course app The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Is Learning Feasible? The Linear Model I - Linear classification and linear regression. Error and Noise - The principled choice of error measures. Training versus Testing - The difference between training and testing in mathematical terms. Theory of Generalization - How an infinite model can learn from a finite sample. The VC Dimension - A measure of what it takes a model to learn. Bias-Variance Tradeoff - Breaking down the learning performance into competing quantities.
Bayesian credible intervals in the mainstream medical literature | StatsBlogs.com (This article was originally published at BioStatMatt » statistics, and syndicated at StatsBlogs.) I have sometimes heard complaints from collaborators that it will be impossible to have their work published in the mainstream literature unless a p-value is reported. This post is to report yet another counterexample that was recently published; a meta-analysis for the odds of perioperative bleeding complications in patients taking one of several anticoagulant/antiplatelet drugs. In this study1 (published by Circulation: Arrhythmia and Electrophysiology), the statistical evidence was reported using Bayesian point estimates and credible intervals. Bayesian analysis formalizes the notion of prior evidence about quantities under study, that is, the evidence at hand before an experiment is carried out. Another complication that arises when we take a Bayesian approach is justification of the selected prior distribution: 1Michael L. Please comment on the article here: BioStatMatt » statistics
Learning From Data - Online Course (MOOC) A real Caltech course, not a watered-down version on YouTube & iTunes Free, introductory Machine Learning online course (MOOC) Taught by Caltech Professor Yaser Abu-Mostafa [article]Lectures recorded from a live broadcast, including Q&APrerequisites: Basic probability, matrices, and calculus8 homework sets and a final examDiscussion forum for participantsTopic-by-topic video library for easy review Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. What is learning? Live Lectures This course was broadcast live from the lecture hall at Caltech in April and May 2012. The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Is Learning Feasible? The Linear Model I - Linear classification and linear regression. Error and Noise - The principled choice of error measures. Training versus Testing - The difference between training and testing in mathematical terms.
11Ants Analytics – Advanced Predictive Analytics, Customer Analytics, Predictive Modelling Software Introducing PredictionIO PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. Building a production-grade engine to predict users’ preferences and personalize content for them used to be time-consuming. Not anymore with PredictionIO’s latest v0.7 release. We are going to show you how PredictionIO streamlines the data process and make it friendly for developers and production deployment. Prerequisite First, let’s explain a few terms we use in PredictionIO. Apps Apps in PredictionIO are not apps with program code. Engines Engines are logical identities that an external application can interact with via the API. Algorithms Algorithms are actual computation code that generates prediction models. Getting Hands-on Preparing the Environment Assuming a recent 64-bit Ubuntu Linux is installed, the first step is to install Java 7 and MongoDB. Note: any recent 64-bit Linux should work. Adding Engines Enjoy!
Feature selection In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features for use in model construction. The central assumption when using a feature selection technique is that the data contains many redundant or irrelevant features. Redundant features are those which provide no more information than the currently selected features, and irrelevant features provide no useful information in any context. improved model interpretability,shorter training times,enhanced generalisation by reducing overfitting. Feature selection is also useful as part of the data analysis process, as it shows which features are important for prediction, and how these features are related. Introduction[edit] Wrapper methods use a predictive model to score feature subsets. Subset selection[edit] Subset selection evaluates a subset of features as a group for suitability. Optimality criteria[edit]
Machine Learning Course Description In this course, you'll learn about some of the most widely used and successful machine learning techniques. You'll have the opportunity to implement these algorithms yourself, and gain practice with them. Familiarity with programming, basic linear algebra (matrices, vectors, matrix-vector multiplication), and basic probability (random variables, basic properties of probability) is assumed. An Efficient Density based Improved K- Medoids Clustering algorithm An efficient density based improved k-medoids clustering algorithm seminar topic explains about extracting information from raw data using clustering methods. In order to extract information from raw data kmedoids is the basic method used. Though they are easy to implement but they are many drawbacks in these methods. In order to overcome these drawbacks we propose a density based k-medois clustering method which performs better than DBSCAN in terms of quality. For more information on this topic students can download reference material from below link. Computer science and information technology students can find related projects, seminar topics , projects with source code from this site for free download. download An Efficient Density based Improved K- Medoids Clustering algorithm related information from this link. The following two tabs change content below. Hi, We kasarla shanthan and Ramesh Gavva graduated Btech from VTU / JNTU & former employees of Infrotrack / Wipro Technologies .
Richard Socher - Deep Learning Tutorial Slides Updated Version of Tutorial at NAACL 2013 See Videos High quality video of the 2013 NAACL tutorial version are up here: quality version of the 2012 ACL version: on youtube Abstract Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. Outline References All references we referred to in one pdf file Further Information A very useful assignment for getting started with deep learning in NLP is to implement a simple window-based NER tagger in this exercise we designed for the Stanford NLP class 224N. For your comments, related questions or errata: Save your text first, then fill out captcha, then save again. Wenbo? Hi Richard, I am a big fan of your C S224d? Gebre? Hi Richard, I am building NER System for Tigrigna, one of under resourced Semitic language like Arabic.
Anametrix You want to make better use of data to improve all forms of consumer interactions, from campaign performance and social engagement to web site content and ad planning. It’s the key to determining whether your marketing decisions lead to success. But your data is trapped in dozens of systems, databases, spreadsheets and applications – both inside and outside your organization. Sound familiar? Unify Your Data Anametrix enables marketers like you to bring together and make sense of all your data, so you can focus your time on the analysis that will drive marketing performance. Turn Data into Insights Our cloud-based analytics platform gives you a unified view of your paid-, owned- and earned-media effectiveness to assess marketing effectiveness. With Anametrix, you can: Drive Revenue and Profitability By collecting, analyzing and making sense out of data from virtually any source, Anametrix delivers not just another set of dashboards, but a real decision-support solution.