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Singular Value Decomposition Tutorial
SVD is extraordinarily useful and has many applications such as data analysis, signal processing, pattern recognition, image compression, weather prediction, and Latent Semantic Analysis or LSA (also referred to as Latent Semantic Indexing or LSI).
ECT 584 - Data Mining in Weka
This guide/tutorial uses a detailed example to illustrate some of the basic data preprocessing and mining operations that can be performed using WEKA. It is based on WEKA version 3.4.1. Some of the interface elements and modules may have changed in the most current version of WEKA. You can download the most current version of WEKA from the WEKA Web site. The current version includes a few additional features in the GUI and has a more organized packaging structure for the Java components. You should pay attention to these differences as you go through the tutorial. The differences in packaging structure are particularly important when you are running WEKA from the commandline.
Google Tech Talk Review: Statistical Aspects of Data Mining | A Beautiful WWW
This is a talk series being given at Google by David Mease based on a Master’s level stats course he is teaching this summer at Stanford. Its easy listening if you already have some data mining or stats background.
Statistical Data Mining Tutorials by Andrew Moore
Andrew Moore
K Nearest Neighbors Tutorial
This tutorial is an introduction to an instance based learning called K-Nearest Neighbor or KNN algorithm. KNN is part of supervised learning that has been used in many applications in the field of data mining, statistical pattern recognition, image processing and many others. Some successful applications are including recognition of handwriting, satellite image and EKG pattern. Instead of using sophisticated software or any programming language, I will use only spreadsheet functions of Microsoft Excel, without any macro. You can free download the spreadsheet companion of this tutorial.
MCMC(Markov chain Monte Carlo) tutorial
Markov chain Monte Carlo is a general computing technique that has been widely used in physics,
chemistry, biology, statistics, and computer science. It simulates a Markov chain whose invariant
states follow a given (target) probability in a very high (say millions) dimensional state space.
Essentially, it generates fair samples from a probability which are used for many purposes.
SVD and LSI Tutorial 1: Understanding SVD and LSI
A tutorial on Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI), its advantages, applications and limitations. Covers LSI myths and misconceptions from search engine marketers.
CS 6604: Recommender Systems (Spring 2001)
CS 6604 concentrates on algorithms, methodologies, systems, and larger-scope issues (economic, commercial etc.) pertaining to reducing information overload. The unique aspect of this course will be how it integrates ideas from diverse areas: numerical analysis (strange but true), information systems, human-computer interaction, and algorithmics. Over the past three years, a large body of literature on recommender systems, filtering, and personalization technologies has been developed. Even though the field is driven by commercial trends and industrial developments, many of the ideas are nearing a stage of stabilization when their use is becoming common place (textbook material). CS 6604 will help illustrate the interplay between these different areas and demonstrate how ideas from diverse backgrounds can be combined in novel and sophisticated ways.
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