Most machine learning algorithms have been developed and statistically validated for linearly separable data. Popular examples are linear classifiers like Support Vector Machines (SVMs) or the (standard) Principal Component Analysis (PCA) for dimensionality reduction. However, most real world data requires nonlinear methods in order to perform tasks that involve the analysis and discovery of patterns successfully.

The focus of this article is to briefly introduce the idea of kernel methods and to implement a Gaussian radius basis function (RBF) kernel that is used to perform nonlinear dimensionality reduction via BF kernel principal component analysis (kPCA).

You may be thinking that this title makes no sense at all. ML, AI, ANN and Deep learning have made it into the everyday lexicon and here I am, proclaiming that ML is dead. Well, here is what I mean…

The open sourcing of entire ML frameworks marks the end of a phase of rapid development of tools, and thus marks the death of ML as we have known it so far. The next phase will be marked with ubiquitous application of these tools into software applications. And that is how ML will live forever, because it will seamlessly and inextricably integrate into our lives.

Assessing the fit of a count regression model is not necessarily a straightforward enterprise; often we just look at residuals, which invariably contain patterns of some form due to the discrete nature of the observations, or we plot observed versus fitted values as a scatter plot. Recently, while perusing the latest statistics offerings on ArXiv I came across Kleiber and Zeileis (2016) who propose the rootogram as an improved approach to the assessment of fit of a count regression model. The paper is illustrated using R and the authors’ countreg package (currently on R-Forge only). Here, I thought I’d take a quick look at the rootogram with some simulated species abundance data.

Integrates the theory and applications of statistics using R. A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models.

On Tuesday, analytics company SAS announced Viya, its new analytics and visualization architecture. A SAS spokeswoman said Viya would become the "foundation for all future SAS products."

The goal of Viya is to make analytics more accessible to all users and to better support cloud-native apps and data stored in the cloud.

The announcement came at the 2016 SAS Global Forum, the company's annual conference for SAS users and executives, which took place in Las Vegas. Viya will be available as part of an early adopter program in May, but it will reach general availability sometime in Q3 of 2016.