A lot of Machine Learning (ML) projects consist of fitting a (normally very complicated) function to a dataset with the objective of calculating a number like 1 or 0 (is it spam or not?) for classification problems or a set of numbers (e.g., weekly sales of a product) for regression ones. Yes, it's all about numbers and loads of operations which a computer is very good at. Consider the gender recognition by voice dataset which can be found in this Kaggle page. The objective with this dataset is, when given a speech signal, to identify whether it is from a male or female. This challenge falls under the category of a classification problem.

A/B Testing - In marketing, A/B testing is a simple randomized experiment with two variants, A and B, which are the control and treatment in the controlled experiment. It is a form of statistical hypothesis testing. Other names include randomized controlled experiments, online controlled experiments, and split testing. In online settings, such as web design (especially user experience design), the goal is to identify changes to web pages that increase or maximize an outcome of interest (e.g., click-through rate for a banner advertisement). Adaptive Boosting (AdaBoost) - AdaBoost, short for "Adaptive Boosting", is a machine learning meta-algorithm formulated by Yoav Freund and Robert Schapire who won the prestigious "Gödel Prize" in 2003 for their work.

Let's walk through an example of predictive analytics using a data set that most people can relate to:prices of cars. In this case, we have a data set with historical Toyota Corolla prices along with related car attributes. In predictive models, there is a response variable(also called dependent variable), which is the variable that we are interested in predicting. The independent variables(the predictors also called features in the machine learning community) are one or more numeric variables we are using to predict the response variable. Given we are using a linear regression model, we are assuming the relationship between the independent and dependent variables follow a straight line.

When you Google "Kalman Filter AND Machine Learning", very few interesting references pop up! Perhaps my search terms are not the best, perhaps Fintech guys keep such algorithms close to their vests, perhaps there is not much of work done in bringing these two incredibly powerful tools together... In any case, Part II of my new book, "Systems Analytics: Adaptive Machine Learning workbook" focuses exactly on this merger. Condition monitoring "How is my business doing today?" Preventive maintenance "Predict issues and fix them before adverse business impact".

We generate data when using an ATM, browsing the Internet, calling our friends, buying shoes in our favourite e-shop or posting on Facebook. Which customers should participate in our promotional campaign for a given product in order to maximize response? Which customers should be paid special attention to, as they might be considering resigning from using our services? Is a particular customer trustworthy and does he/she qualify for a mortgage loan? Which customers should participate in our promotional campaign for a given product in order to maximize response?

Posted by Kaz Sato, Staff Developer Advocate, Google and Ray Sakai, Product Manager, Reactive Inc. At GCP NEXT 2016, the biggest Google Cloud Platform event held this year in San Francisco, Jeff Dean, Google Senior Fellow, presented the Cloud Vision API with Cloud Vision Explorer. This amazing demo is now available for anyone and we warmly invite you to give it a try. To recap, Cloud Vision API is an image analysis service that's part of Cloud Platform. It enables you to understand the content of images by encapsulating powerful machine learning models in an easy-to-use REST API.

Group-Lasso (gLasso) identifies important explanatory factors in predicting the response variable by considering the grouping structure over input variables. However, most existing algorithms for gLasso are not scalable to deal with large-scale datasets, which are becoming a norm in many applications. In this paper, we present a divide-and-conquer based parallel algorithm (DC-gLasso) to scale up gLasso in the tasks of regression with grouping structures. DC-gLasso only needs two iterations to collect and aggregate the local estimates on subsets of the data, and is provably correct to recover the true model under certain conditions. We further extend it to deal with overlappings between groups. Empirical results on a wide range of synthetic and real-world datasets show that DC-gLasso can significantly improve the time efficiency without sacrificing regression accuracy.

Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity. In this paper, we jointly analyze fMRI and multi-channel EEG signals collected during an auditory oddball task with the goal of capturing brain activity patterns that differ between patients with schizophrenia and healthy controls. Rather than selecting a single electrode or matricizing the third-order tensor that can be naturally used to represent multi-channel EEG signals, we preserve the multi-way structure of EEG data and use a coupled matrix and tensor factorization (CMTF) model to jointly analyze fMRI and EEG signals. Our analysis reveals that (i) joint analysis of EEG and fMRI using a CMTF model can capture meaningful temporal and spatial signatures of patterns that behave differently in patients and controls, and (ii) these differences and the interpretability of the associated components increase by including multiple electrodes from frontal, motor and parietal areas, but not necessarily by including all electrodes in the analysis.

Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to maintain an oracle $D$ that discriminates between the expert's data distribution and that of the generative model $G$. The generative model is trained to capture the expert's distribution by maximizing the probability of $D$ misclassifying the data it generates. Overall, the system is \emph{differentiable} end-to-end and is trained using basic backpropagation. This type of learning was successfully applied to the problem of policy imitation in a model-free setup. However, a model-free approach does not allow the system to be differentiable, which requires the use of high-variance gradient estimations. In this paper we introduce the Model based Adversarial Imitation Learning (MAIL) algorithm. A model-based approach for the problem of adversarial imitation learning. We show how to use a forward model to make the system fully differentiable, which enables us to train policies using the (stochastic) gradient of $D$. Moreover, our approach requires relatively few environment interactions, and fewer hyper-parameters to tune. We test our method on the MuJoCo physics simulator and report initial results that surpass the current state-of-the-art.

We introduce a novel approach for parallelizing MCMC inference in models with spatially determined conditional independence relationships, for which existing techniques exploiting graphical model structure are not applicable. Our approach is motivated by a model of seismic events and signals, where events detected in distant regions are approximately independent given those in intermediate regions. We perform parallel inference by coloring a factor graph defined over regions of latent space, rather than individual model variables. Evaluating on a model of seismic event detection, we achieve significant speedups over serial MCMC with no degradation in inference quality.