So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

Domino's seems to be trying to master the sneaky tech move. It has deployed an attention-getting, flashy toy that, truth be told, has very little to do with technology and everything to do with making consumers curious enough to order its less-than-delectable pizzas. We've talked about it with its oven cars, a huge fake button that is supposed to be about ordering but really isn't and a social media purchasing campaign that was tricky marketing at its best. I know that, because I think we all should order good-tasting pizzas from local pizzerias, I shouldn't help promote these efforts. But Domino's appears to know how to get to me: It is now using an R2D2-like robot to deliver pizzas.

Ever wondered how your bank knew to call you when a thief attempted to buy a flat-screen TV on your Visa credit card? Or why your mobile banking app sent you an alert after a series of unusual transactions occurred on your Visa account at a gas station hundreds of miles from your home? Whether you insert, swipe, touch, click, or wave to make a payment, our predictive analytics, known as Visa Advanced Authorization, are monitoring in real-time for suspicious activity. Since it was launched just over a decade ago, Visa Advanced Authorization has grown increasingly effective at spotting the tiny percentage of suspicious transactions from the roughly 150 million payments that flow through the Visa network each day. In fact, Visa's system-wide fraud rate has declined by two-thirds over the last two decades--to less than 6 cents out of every 100 transacted--even as transaction volume has increased by more than 1,000 percent.

The SETI Institute has partnered with NASA HQ, NVIDIA and the Asteroid Grand Challenge for a summer research project aimed at pairing young planetary scientists with early-career machine learning software developers to "hack" various datasets pertaining to the tracking and cataloging of Near Earth Objects. The idea is to see what can be gleaned from such datasets by applying some of the latest developments in machine learning to analyze the data in new and innovative ways. SETI is looking for more applicants to the project from the planetary science community, where the target is PhD candidates or postdocs. The project will run for 6 weeks this summer, and will be hosted at the SETI Institute. Participants will be housed at dormitories at NASA Ames.

To create an inclusive community that successfully tackles issues like discrimination, we need open lines of communication between faculty and students. When that communication breaks down, a misunderstanding can turn ugly and prevent real progress. We saw an example of this recently when Professor Satyen Kale assigned his machine learning class a project: to train classifiers on the New York Police Department's stop-and-frisk dataset. Stop-and-frisk was a controversial NYPD interrogation program that disproportionately targeted young black and Hispanic men and was ruled unconstitutional and discriminatory by a federal court in 2013. The assignment, satirically titled "Help design RoboCop!" was an exercise using records of searches conducted by the NYPD.

In just a few years, it will provide customer service in airports, hospitals, train stations and other facilities, speaking four languages so that it can even serve the masses of foreign tourists streaming into Japan. It is Hitachi's Emiew3 -- a smaller, faster and more agile competitor unveiled Friday. The new robot marks the third generation, and first commercially viable member, of a series that began with an experimental model in 2005. The company seeks to put it on the market in 2018. In a demonstration Friday, an Emiew3 prototype surveyed its surroundings and approached an actress playing a lost foreigner.

We derive generalized estimators for a number of spatial statistics that have been used in the analysis of spatially resolved omics data, such as Ripley's K, H and L functions, clustering index, and degree of clustering, which allow these statistics to be calculated on data modelled by arbitrary random measures (RMs). Our estimators generalize those typically used to calculate these statistics on point process data, allowing them to be calculated on RMs which assign continuous values to spatial regions, for instance to model protein intensity. The clustering index (H*) compares Ripley's H function calculated empirically to its distribution under complete spatial randomness (CSR), leading us to consider CSR null hypotheses for RMs which are not point-processes when generalizing this statistic. We thus consider restricted classes of completely random measures which can be simulated directly (Gamma processes and Marked Poisson Processes), as well as the general class of all CSR RMs, for which we derive an exact permutation-based H* estimator. We establish several properties of the estimators, including bounds on the accuracy of our general Ripley K estimator, its relationship to a previous estimator for the cross-correlation measure, and the relationship of our generalized H* estimator to previous statistics. To test the ability of our approach to identify spatial patterning, we use Fluorescent In Situ Hybridization (FISH) and Immunofluorescence (IF) data to probe for mRNA and protein subcellular localization patterns respectively in polarizing mouse fibroblasts on micropattened cells. We observe correlated patterns of clustering over time for corresponding mRNAs and proteins, suggesting a deterministic effect of mRNA localization on protein localization for several pairs tested, including one case in which spatial patterning at the mRNA level has not been previously demonstrated.

We propose a novel method to determine the dissimilarity between subjects for functional data clustering. Spline smoothing or interpolation is common to deal with data of such type. Instead of estimating the best-representing curve for each subject as fixed during clustering, we measure the dissimilarity between subjects based on varying curve estimates with commutation of smoothing parameters pair-by-pair (of subjects). The intuitions are that smoothing parameters of smoothing splines reflect inverse signal-to-noise ratios and that applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close. The effectiveness of our proposal is shown through simulations comparing to other dissimilarity measures. It also has several pragmatic advantages. First, missing values or irregular time points can be handled directly, thanks to the nature of smoothing splines. Second, conventional clustering method based on dissimilarity can be employed straightforward, and the dissimilarity also serves as a useful tool for outlier detection. Third, the implementation is almost handy since subroutines for smoothing splines and numerical integration are widely available. Fourth, the computational complexity does not increase and is parallel with that in calculating Euclidean distance between curves estimated by smoothing splines.

How does one objectively measure the performance of an individual offensive lineman in the NFL? The existing literature proposes various measures that rely on subjective assessments of game film, but has yet to develop an objective methodology to evaluate performance. Using a variety of statistics related to an offensive lineman's performance, we develop a framework to objectively analyze the overall performance of an individual offensive lineman and determine specific linemen who are overvalued or undervalued relative to their salary. We identify eight players across the 2013-2014 and 2014-2015 NFL seasons that are considered to be overvalued or undervalued and corroborate the results with existing metrics that are based on subjective evaluation. To the best of our knowledge, the techniques set forth in this work have not been utilized in previous works to evaluate the performance of NFL players at any position, including offensive linemen.

Coregulation of the expression of groups of genes has been extensively demonstrated empirically in bacterial and eukaryotic systems. Such coregulation can arise through the use of shared regulatory motifs, which allow the coordinated expression of modules (and module groups) of functionally related genes across the genome. Coregulation can also arise through the physical association of multi-gene complexes through chromosomal looping, which are then transcribed together. We present a general formalism for modeling coregulation rules in the framework of Random Boolean Networks (RBN), and develop specific models for transcription factor networks with modular structure (including module groups, and multi-input modules (MIM) with autoregulation) and multi-gene complexes (including hierarchical differentiation between multi-gene complex members). We develop a mean-field approach to analyse the stability of large networks incorporating coregulation, and show that autoregulated MIM and hierarchical gene-complex models can achieve greater stability than networks without coregulation whose rules have matching activation frequency. We provide further analysis of the stability of small networks of both kinds through simulations. We also characterize several general properties of the transients and attractors in the hierarchical coregulation model, and show using simulations that the steady-state distribution factorizes hierarchically as a Bayesian network in a Markov Jump Process analogue of the RBN model.