Who will remain a loyal customer and who won't? Which messages are most effective with which segments? How can customer value be maximized? This book supplies powerful tools for extracting the answers to these and other crucial business questions from the corporate databases where they lie buried. In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business.

I am working on some practical articles on variable selection, especially in the context of step-wise linear regression and logistic regression. One thing I noticed while preparing some examples is that summaries such as model quality (especially out of sample quality) and variable significances are not quite as simple as one would hope (they in fact lack a lot of the monotone structure or submodular structure that would make things easy). That being said we have a lot of powerful and effective heuristics to discuss in upcoming articles. I am going to leave such positive results for my later articles and here concentrate on an instructive technical negative result: picking a good subset of variables is theoretically quite hard. When we say something is "theoretically hard" we mean we can contrive examples of it that encode instances of other problems thought to be hard.

Machine learning (ML) is all the hotness right now. There is literally a new story every week about a complicated problem that was solved using ML. Most of my coworkers and friends in the industry have expressed interest in learning about ML, but have not been able gain a foot hold. I believe the problem is that the current books and online classes are overwhelming; scaring away students before they have a chance to build up confidence. The problem I had when I started learning ML was that the tutorials insisted on teaching the math that is fundamental for machine learning to work.

Ted Dunning, Chief Applications Architect for MapR, presented a session titled: "Some Important Streaming Algorithms You Should Know About" at the Spark Summit 2015 conference held in San Francisco. During the session, he highlighted some newer streaming algorithms such as t-digest and streaming k-means. This article is adapted from his talk. The key thing about streaming algorithms is they have to be approximate algorithms. There are a few things that you can compute exactly in a streaming fashion, but there are lots of important things that you can't compute that way, so we have to approximate. Most important aggregates can be approximated online.

Deep neural networks have been shown to suffer from a surprising weakness: their classification outputs can be changed by small, non-random perturbations of their inputs. This adversarial example phenomenon has been explained as originating from deep networks being "too linear" (Goodfellow et al., 2014). We show here that the linear explanation of adversarial examples presents a number of limitations: the formal argument is not convincing, linear classifiers do not always suffer from the phenomenon, and when they do their adversarial examples are different from the ones affecting deep networks. We propose a new perspective on the phenomenon. We argue that adversarial examples exist when the classification boundary lies close to the submanifold of sampled data, and present a mathematical analysis of this new perspective in the linear case. We define the notion of adversarial strength and show that it can be reduced to the deviation angle between the classifier considered and the nearest centroid classifier. Then, we show that the adversarial strength can be made arbitrarily high independently of the classification performance due to a mechanism that we call boundary tilting. This result leads us to defining a new taxonomy of adversarial examples. Finally, we show that the adversarial strength observed in practice is directly dependent on the level of regularisation used and the strongest adversarial examples, symptomatic of overfitting, can be avoided by using a proper level of regularisation.

Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list. Previous work has yielded several approaches to this problem, with theoretical results in the setting where the graph is drawn from a stochastic block model (SBM), including a vertex nomination analogue of the Bayes optimal classifier. In this paper, we prove that maximum likelihood (ML)-based vertex nomination is consistent, in the sense that the performance of the ML-based scheme asymptotically matches that of the Bayes optimal scheme. We prove theorems of this form both when model parameters are known and unknown. Additionally, we introduce and prove consistency of a related, more scalable restricted-focus ML vertex nomination scheme. Finally, we incorporate vertex and edge features into ML-based vertex nomination and briefly explore the empirical effectiveness of this approach.

Summary: We show in illustrations how the machine learning'training' process happens in Tensorflow, and tie them back to the Tensorflow code. This paves the way for discussing'training' variations, namely stochastic/mini-batch/batch, and adaptive learning rate gradient descent. The'training' variation code snippets presented serve to reinforce the understanding of the role of Tensorflow placeholders. In the previous article, we used Tensorflow (TF) to build and learn a linear regression model with a single feature so that given a feature value (house size/sqm), we can predict the outcome (house price/). In machine learning (ML) literature, we come across the term'training' very often, let us literally look at what that means in TF.

Machine and statistical learning wizards are becoming more eager to perform analysis with Spark ML library if this is only possible. It's trendy, posh, spicy and gives the feeling of doing state of the art machine learning and being up to date with the newest computational trends. It is even more sexy and powerful when computations can be performed on the extraordinarily enormous computation cluster - let's say 100 machines on YARN hadoop cluster makes you the real data cruncher! In this post I present sparklyr package (by RStudio), the connector that will transform you from a regular R user, to the supa! Moreover, I present how I have extended the interface to K-means procedure, so that now it is also possible to compute cost for that model, which might be beneficial in determining the number of clusters in segmentation problems.

The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimator leads to the well-known unscented Kalman filter (UKF). Although the UKF works very well in Gaussian noises, its performance may deteriorate significantly when the noises are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises. To improve the robustness of the UKF against impulsive noises, a new filter for nonlinear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF). In MCUF, the UT is applied to obtain the prior estimates of the state and covariance matrix, and a robust statistical linearization regression based on the maximum correntropy criterion (MCC) is then used to obtain the posterior estimates of the state and covariance. The satisfying performance of the new algorithm is confirmed by two illustrative examples.

Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present. We show that ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced cluster sizes since they tend to emphasize cut sizes over cut values. We propose a graph partitioning problem that seeks minimum cut partitions under minimum size constraints on partitions to deal with imbalanced cluster sizes. Our approach parameterizes a family of graphs by adaptively modulating node degrees on a fixed node set, yielding a set of parameter dependent cuts reflecting varying levels of imbalance. The solution to our problem is then obtained by optimizing over these parameters. We present rigorous limit cut analysis results to justify our approach and demonstrate the superiority of our method through experiments on synthetic and real datasets for data clustering, semi-supervised learning and community detection.