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. Therein lies the premise and the promise of the field of machine learning and the project this article introduces: Apache Mahout (see Related topics). 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 Features
Lecture 6: Collaborative Filtering / Information Extraction Lecture 6: Collaborative Filtering / Information Extraction Tao Yang's Lecture ExpertRank: Ranking system for Ask.com. See US Patent Application 7028026 by Tao Yang, Wei Wang, and Apostolos Gerasoulis. Retrieve documents from inverted file. Cluster documents by content and by link structure Apply a hub/authority analysis to each clusters. Required Reading: Chakrabarti, sec 4.5 Evaluating collaborative filtering recommender systems By Jonathan Herlocker, Joseph Konstan, Loren Terveen, and John Reidl, ACM Transations on Information Systems, vol. 22, No. 1, 2004, pp. 5-53. Unsupervised Named-Entity Extraction from the Web. Additional Reading Amazon.com Recommendations: Item to Item Collaborative Filtering by Greg Linden, Brent Smith and Jeremy York, IEEE Internet Computing January-February 2003. Collaborative Filtering Example: Terms and Documents We say that document D is relevant to query term T if D contains T. Example: Personal preferences General issues in either of these: 1.
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!
Geeking with Greg 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 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. The Linear Model II - More about linear models. Neural Networks - A biologically inspired model. Validation - Taking a peek out of sample.
database - How to create my own recommendation engine 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.
About GroupLens | GroupLens Research 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. A movie recommendation case will be used for illustration purpose. We want to offer “Top 10 Personalized Movie Recommendation” for each user. 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 Adding Engines Enjoy!
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. You will also learn some of practical hands-on tricks and techniques (rarely discussed in textbooks) that help get learning algorithms to work well. This is an "applied" machine learning class, and we emphasize the intuitions and know-how needed to get learning algorithms to work in practice, rather than the mathematical derivations. Familiarity with programming, basic linear algebra (matrices, vectors, matrix-vector multiplication), and basic probability (random variables, basic properties of probability) is assumed.
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.