Doing Data Science: A Kaggle Walkthrough Part 6 - Creating a Model Top Machine Learning Libraries for Javascript Improving Nudity Detection and NSFW Image Recognition History of Data Mining Predictive Analytics World in October: Government, Business, Financial, Healthcare Software 5 More Machine Learning Projects You Can No Longer Overlook BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark Achieving End-to-end Security for Apache Spark with Databricks Predicting purchases at retail stores using HPE Vertica and Dataiku DSS Tutorials, Overviews, How-Tos Mining Twitter Data with Python Part 4: Rugby and Term Co-occurrences Ten Simple Rules for Effective Statistical Practice: An Overview Mining Twitter Data with Python Part 3: Term Frequencies Opinions The Big Data Ecosystem is Too Damn Big An Inside Update on Natural Language Processing From Research to Riches: Data Wrangling Lessons from Physical and Life Science News Top Stories, June 20-26: New Machine Learning Book, Free Draft Chapters; Machine Learning Trends & Future of A.I. Webcasts and Webinars Webinar, Jun 30: Introducing Anaconda Mosaic: Visualize. Bank of Ireland: Senior Data Scientist within the Advanced Analytics Team DuPont Pioneer: Data Scientist - Encirca Academic U. of Iowa: Business Analytics & Information Systems, Lecturer U. of Iowa: Lecturer: Business Analytics & Information Systems Top Tweets Top KDnuggets tweets, Jun 15-21: Predicting UEFA Euro2016; Visual Explanation of Backprop for Neural Nets Quote "Everything at scale in this world is going to be managed by algorithms and data ... every business will be an algorithmic business."

Click on the first attribute in the dataset in the "Attributes" panel. Take note of the details in the "Selected attribute" panel.

As databases keep growing unabatedly, so too has the need for smart data mining. For a competitive edge in business, it helps to be able to analyse your data in unique ways. This text gives you a thorough education in state of the art data mining. The extensive problem sets are well suited for the student. These often expand on concepts in the narrative, and are worth tackling.

In this study, we present and analyze a framework for geometric and topological estimation for mapping of unknown environments. We consider agents mimicking motion behaviors of cyborg insects, known as biobots, and exploit coordinate-free local interactions among them to infer geometric and topological information about the environment, under minimal sensing and localization constraints. Local interactions are used to create a graphical representation referred to as the encounter graph. A metric is estimated over the encounter graph of the agents in order to construct a geometric point cloud using manifold learning techniques. Topological data analysis (TDA), in particular persistent homology, is used in order to extract topological features of the space and a classification method is proposed to infer robust features of interest (e.g. existence of obstacles). We examine the asymptotic behavior of the proposed metric in terms of the convergence to the geodesic distances in the underlying manifold of the domain, and provide stability analysis results for the topological persistence. The proposed framework and its convergences and stability analysis are demonstrated through numerical simulations and experiments.

We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets ("bags") of unlabeled instances with constraints on label proportions. We relax the unrealistic assumption of known label proportions, made in previous work; instead, we assume only to have upper and lower bounds, and constraints on bag differences. We motivate the problem, propose an intuitive formulation and algorithm, and apply our methods to real-world scenarios. Across several domains, we show how using only proportion constraints and no labeled examples, we can achieve surprisingly high accuracy. In particular, we demonstrate how to predict income level using rough stereotypes and how to perform sentiment analysis using very little information. We also apply our method to guide exploratory analysis, recovering geographical differences in twitter dialect.

In this paper we consider the composite self-concordant (CSC) minimization problem, which minimizes the sum of a self-concordant function $f$ and a (possibly nonsmooth) proper closed convex function $g$. The CSC minimization is the cornerstone of the path-following interior point methods for solving a broad class of convex optimization problems. It has also found numerous applications in machine learning. The proximal damped Newton (PDN) methods have been well studied in the literature for solving this problem that enjoy a nice iteration complexity. Given that at each iteration these methods typically require evaluating or accessing the Hessian of $f$ and also need to solve a proximal Newton subproblem, the cost per iteration can be prohibitively high when applied to large-scale problems. Inspired by the recent success of block coordinate descent methods, we propose a randomized block proximal damped Newton (RBPDN) method for solving the CSC minimization. Compared to the PDN methods, the computational cost per iteration of RBPDN is usually significantly lower. The computational experiment on a class of regularized logistic regression problems demonstrate that RBPDN is indeed promising in solving large-scale CSC minimization problems. The convergence of RBPDN is also analyzed in the paper. In particular, we show that RBPDN is globally convergent when $g$ is Lipschitz continuous. It is also shown that RBPDN enjoys a local linear convergence. Moreover, we show that for a class of $g$ including the case where $g$ is Lipschitz differentiable, RBPDN enjoys a global linear convergence. As a striking consequence, it shows that the classical damped Newton methods [22,40] and the PDN [31] for such $g$ are globally linearly convergent, which was previously unknown in the literature. Moreover, this result can be used to sharpen the existing iteration complexity of these methods.

We propose a general model explanation system (MES) for "explaining" the output of black box classifiers. This paper describes extensions to Turner (2015), which is referred to frequently in the text. We use the motivating example of a classifier trained to detect fraud in a credit card transaction history. The key aspect is that we provide explanations applicable to a single prediction, rather than provide an interpretable set of parameters. We focus on explaining positive predictions (alerts). However, the presented methodology is symmetrically applicable to negative predictions.

I plan to use this book as a supplement to an MBA-level course on Business Analytics. It is extremely well organized, clearly written and introduces all of the basic ideas quite well. It covers the fundamentals of "data mining" and "data visualization", classification and prediction, logistic regression, cluster analysis, and forecasting. It comes with a 6 month license to XLMiner which is an add-in to Excel and access to data for a number of cases. First of all, there are an incredible number of typos in the book -- far too many for a Second Edition.

The moment you say K-means, it indicates you have knowledge of number of clusters (i.e. K) in advance and you are not going to change it later on. I believe, what you intend to ask is, is there an automatic way to adaptively changing the number of clusters as new data arrives. Normally, in online clustering you start with one sample (hence one cluster) and based on *some* criteria, you either merge or break clusters to adaptively change number of clusters; this process is not online k-means clustering but only online clustering. In online K-means clustering, you update the cluster center information for every sample and do not wait for all the samples to arrive (or else it becomes traidtional offline k-means clustering).

Editor's Note: Don't miss our upcoming Free Code Friday on July 1st. Carol will give an overview of machine learning with Apache Spark's MLlib, and you'll also learn how MLlib decision trees can be used to predict flight delays. Decision trees are widely used for the machine learning tasks of classification and regression. In this blog post, I'll help you get started using Apache Spark's MLlib machine learning decision trees for classification. In general, machine learning may be broken down into two classes of algorithms: supervised and unsupervised.