It's pretty clear that the next big battleground for public cloud providers will involve artificial intelligence. Just as companies like Amazon Web Services made it possible for ten-person startups to take advantage of world-class computing infrastructure, so too will the big cloud providers compete to provide artificial intelligence expertise to companies that can't afford to duplicate the advanced machine-learning research already underway. Swami Sivasubramanian, vice president of Amazon AI, is one of the key drivers of AI research for AWS. Cloud rivals like Google and Microsoft have signaled quite clearly that they will attempt to compete for the cloud workloads of the future by pushing the envelope of AI and machine-learning research and abstracting that effort for their cloud customers, and AWS must at least match those efforts to stay on top. Sivasubramian will be talking about Amazon's work in this area at our Cloud Tech Summit this Wednesday in Bellevue, and I recently caught up with him to get a preview of his talk.

In this one, you will learn how to create a Neural Network (NN) and use it for deciding whether a student has alcohol consumption problems. Do students drink too much? How can you predict that? How much too much is exactly? Those questions might be difficult to answer, yet we can start somewhere.

PALO ALTO, Calif., June 06, 2017 --Cloudera, Inc., (NYSE: CLDR), the leading provider of the modern platform for machine learning and advanced analytics built on the latest open source technologies, announced that Inova Translational Medicine Institute (ITMI), a global leading medical research institute, has deployed Cloudera Enterprise to securely analyze massive collections of clinical and genomic data at unprecedented speeds and scale for faster innovations in translational medicine research. As part of the Inova Center for Personalized Health (ICPH), ITMI's team of leading scientists, researchers, analysts and collaborators use machine learning algorithms on terabytes of clinical and genomic information to identify the genetic links to diseases. They make discoveries from the data insights and, in collaboration with the treating physician, develop personalized treatment plans for patients. This approach is also known as precision medicine and has the power to help patients live longer, healthier lives. Genetics plays a role in the majority of leading causes of death in the United States, including heart disease, cancer and diabetes.

This is an interesting course on applications of linear algebra in data science. The course will first take you through fundamentals of linear algebra. Then, it will introduce you to applications of linear algebra for recognizing handwritten numbers, ranking of sports team along with online codes. The course is open for enrollment.

We do know, however, that talking seems to help - especially under the guidance of a licensed mental health professional. But therapy is expensive, inconvenient, and often hard to approach. A recent estimate suggests that of the roughly one in five Americans suffering from mental illness, close to two thirds have gone at least a year without treatment . Several Silicon Valley-style approaches to the problem have emerged: There are apps that replace the traditional psychiatry office with texting, and chat-rooms where you can discuss your problems anonymously online. The newest of these tech-based treatments is Woebot, an artificial intelligence-powered chatbot designed using cognitive behavioral therapy, one of the most heavily-researched clinical approaches to treating depression.

There's a new chatbot in town, and it only wants to talk about mental health. Meet Woebot, now available to anyone via Facebook Messenger looking for some supportive talk to deal with anxiety or depression. Rather than augmenting a real therapist or even a non-clinical person, Woebot is wholly robotic; open to engage with an individual as often or as little as they want depending on their needs. San Francisco-based Woebot Labs created the tool –originally intended for college students but later expanded to all adults – based on cognitive behavioral therapy techniques. Anyone with Facebook Messenger can search for Woebot and begin sending messages, effectively bringing Woebot to life.

Astronomical image observation is plagued by the fact the the observed image is the result of a convolution between the observed object and what the astronomers call a Point Spread Function (PSF) [1] [2]. In addition to the convolution the image is also polluted by noise that is due to the low energy of the observed objects (photon noise) or to the sensor. The PSF is usually known a priori, thanks to a physical model for the telescope of estimation from known objects. State of the art approaches in astronomical image reconstruction aim at solving an optimization problem that encodes both a data fitting (with observation and PSF) and a regularization term that promote wanted properties in the images [1], [3], [4]. Still, solving a large optimization problem for each new image can be costly and might not be practical in the future. Indeed in the coming years several new generations of instruments such as the Square kilometer Array [5] will provide very large images (both in spatial and spectral dimensions) that will need to be processed efficiently. The most successful image reconstruction approaches rely on convex optimization [3], [4], [6] and are all based on gradient [7] or proximal splitting gradient descent [8]. Interestingly those methods have typically a linear convergence, meaning that the number of iterations necessary to reach a given precision is proportional to the dimension n of the problem [9], where n is the number of pixels.

In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at the same time obtain the results efficiently in a timely manner for providing good user experience and saving computational costs. Valuable prior research has been conducted for learning to efficiently rank like the cascade ranking (learning) model, which uses a sequence of ranking functions to progressively filter some items and rank the remaining items. However, most existing research of learning to efficiently rank in search is studied in a relatively small computing environments with simulated user queries. This paper presents novel research and thorough study of designing and deploying a Cascade model in a Large-scale Operational E-commerce Search application (CLOES), which deals with hundreds of millions of user queries per day with hundreds of servers. The challenge of the real-world application provides new insights for research: 1). Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2). Effectiveness of e-commerce search involves multiple types of user behaviors such as click and purchase, while most existing cascade ranking in search only models the click behavior. Based on these observations, a novel cascade ranking model is designed and deployed in an operational e-commerce search application. An extensive set of experiments demonstrate the advantage of the proposed work to address multiple factors of effectiveness, efficiency and user experience in the real-world application.

We present a Distributionally Robust Optimization (DRO) approach to outlier detection in a linear regression setting, where the closeness of probability distributions is measured using the Wasserstein metric. Training samples contaminated with outliers skew the regression plane computed by least squares and thus impede outlier detection. Classical approaches, such as robust regression, remedy this problem by downweighting the contribution of atypical data points. In contrast, our Wasserstein DRO approach hedges against a family of distributions that are close to the empirical distribution. We show that the resulting formulation encompasses a class of models, which include the regularized Least Absolute Deviation (LAD) as a special case. We provide new insights into the regularization term and give guidance on the selection of the regularization coefficient from the standpoint of a confidence region. We establish two types of performance guarantees for the solution to our formulation under mild conditions. One is related to its out-of-sample behavior, and the other concerns the discrepancy between the estimated and true regression planes. Extensive numerical results demonstrate the superiority of our approach to both robust regression and the regularized LAD in terms of estimation accuracy and outlier detection rates.

The widespread use of machine learning to make consequential decisions about individual citizens (including in domains such as credit, employment, education and criminal sentencing [3, 4, 26, 29]) has been accompanied by increased reports of instances in which the algorithms and models employed can be unfair or discriminatory in a variety of ways [2, 30]. As a result, research on fairness in machine learning and statistics has seen rapid growth in recent years [1, 5-7, 9-11, 13, 14, 18-21, 25, 27], and several mathematical formulations have been proposed as metrics of (un)fairness for a number of different learning frameworks. While much of the attention to date has focused on (binary) classification settings, where standard fairness notions include equal false positive or negative rates across different populations, less attention has been paid to fairness in (linear and logistic) regression settings, where the target and/or predicted values are continuous, and the same value may not occur even twice in the training data. In this work, we introduce a rich family of fairness metrics for regression models that take the form of a fairness regularizer and apply them to the standard loss functions for linear and logistic regression. Since these loss functions and our fairness regularizer are convex, the combined objective functions obtained from our framework are also convex, and thus permit efficient optimization. Furthermore, our family of fairness metrics covers the spectrum from the type of group fairness that is common in classification formulations (where e.g.