Machine Learning in Gradient Descent In Machine Learning, gradient descent is a very popular learning mechanism that is based on a greedy, hill-climbing approach. Gradient Descent The basic idea of Gradient Descent is to use a feedback loop to adjust the model based on the error it observes (between its predicted output and the actual output). The adjustment (notice that there are multiple model parameters and therefore should be considered as a vector) is pointing to a direction where the error is decreasing in the steepest sense (hence the term "gradient"). Notice that we intentionally leave the following items vaguely defined so this approach can be applicable in a wide range of machine learning scenarios.

The importance of simulating the extremes Simulation is commonly used by statisticians/data analysts to: (1) estimate variability/improve predictors , (2) to evaluate the space of potential outcomes , and (3) to evaluate the properties of new algorithms or procedures. Over the last couple of days, discussions of simulation have popped up in a couple of different places. First, the reviewers of a paper that my student is working on had asked a question about the behavior of the method in different conditions.

Data Mining: Text Mining, Visualization and Social Media The Turing Test for artificial intelligence is a reasonably well understood idea: if, through a written form of communication, a machine can convince a human that it too is a human, then it passes the test. The elegance of this approach (which I believe is its primary attraction) is that it avoids any troublesome definition of intelligence and appeals to an innate ability in humans to detect entities which are not 'one of us'. This form of AI is the one that is generally presented in entertainment (films, novels, etc.). However, to an engineer, there are some problems with this as the accepted popular idea of artificial intelligence. Reading and Text Mining a PDF-File in R 0inShare Here is an R-script that reads a PDF-file to R and does some text mining with it:

Technical Methods Report: Guidelines for Multiple Testing in Impact Evaluations - Appendix B: Introduction to Multiple Testing This appendix introduces the hypothesis testing framework for this report, the multiple testing problem, statistical methods to adjust for multiplicity, and some concerns that have been raised about these solutions. The goal is to provide an intuitive, nontechnical discussion of key issues related to this complex topic to help education researchers apply the guidelines presented in the report. A comprehensive review of the extensive literature in this area is beyond the scope of this introductory discussion. The focus is on continuous outcomes, but appropriate procedures are highlighted for other types of outcomes (such as binary outcomes).

Blog An introductory comparison of using the two languages. Background R was made especially for data analysis and graphics. SQL was made especially for databases. They are allies. The data structure in R that most closely matches a SQL table is a data frame. An Introduction to WEKA - Machine Learning in Java WEKA (Waikato Environment for Knowledge Analysis) is an open source library for machine learning, bundling lots of techniques from Support Vector Machines to C4.5 Decision Trees in a single Java package. My examples in this article will be based on binary classification, but what I say is also valid for regression and in many cases for unsupervised learning. Why and when would you use a library? I'm not a fan of integrating libraries and frameworks just because they exist; but machine learning is something where you have to rely on a library if you're using codified algorithms as they're implemented more efficiently than what you and I can possibly code in an afternoon. Efficiency means a lot in machine learning as supervised learning is one of the few programs that is really CPU-bound and can't be optimized further with I/O improvements.

Statistical significance for genomewide studies Author Affiliations Edited by Philip P. Green, University of Washington School of Medicine, Seattle, WA, and approved May 30, 2003 (received for review January 28, 2003) Big Data, Plainly Spoken (aka Numbers Rule Your World) Two years ago, Wired breathlessly extolled the virtues of A/B testing (link). A lot of Web companies are in the forefront of running hundreds or thousands of tests daily. The reality is that most A/B tests fail. A/B tests fail for many reasons. Typically, business leaders consider a test to have failed when the analysis fails to support their hypothesis. "We ran all these tests varying the color of the buttons, and nothing significant ever surfaced, and it was all a waste of time!"

Official VideoLectures.NET Blog » 100 most popular Machine Learning talks at VideoLectures.Net Enjoy this weeks list! 26971 views, 1:00:45, Gaussian Process Basics, David MacKay, 8 comments7799 views, 3:08:32, Introduction to Machine Learning, Iain Murray16092 views, 1:28:05, Introduction to Support Vector Machines, Colin Campbell, 22 comments5755 views, 2:53:54, Probability and Mathematical Needs, Sandrine Anthoine, 2 comments7960 views, 3:06:47, A tutorial on Deep Learning, Geoffrey E. Hinto3858 views, 2:45:25, Introduction to Machine Learning, John Quinn, 1 comment13758 views, 5:40:10, Statistical Learning Theory, John Shawe-Taylor, 3 comments12226 views, 1:01:20, Semisupervised Learning Approaches, Tom Mitchell, 8 comments1596 views, 1:04:23, Why Bayesian nonparametrics?, Zoubin Ghahramani, 1 comment11390 views, 3:52:22, Markov Chain Monte Carlo Methods, Christian P. Robert, 5 comments3153 views, 2:15:00, Data mining and Machine learning algorithms, José L.

Bayesian Model Averaging Home Page Bayesian Model Averaging Home Page Bayesian Model Averaging is a technique designed to help account for the uncertainty inherent in the model selection process, something which traditional statistical analysis often neglects. By averaging over many different competing models, BMA incorporates model uncertainty into conclusions about parameters and prediction. BMA has been applied successfully to many statistical model classes including linear regression, generalized linear models, Cox regression models, and discrete graphical models, in all cases improving predictive performance. Details on these applications can be found in the papers below.

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