Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Machine learning algorithms are a very large part of machine learning. You have to understand how they work to make any progress in the field. In this post you will discover a 14-part machine learning algorithms mini course that you can follow to finally understand machine learning algorithms. We are going to cover a lot of ground in this course and you are going to have a great time. Machine Learning Algorithms Mini-Course Photo by Jared Tarbell, some rights reserved. Before we get started, let's make sure you are in the right place. This mini-course will take you on a guided tour of machine learning algorithms from foundations and through 10 top techniques.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Fresh off its promotion at Facebook's F8 developer conference, Poncho announced today that it has raised 2 million for its personalized weather forecasting service. The round was led by Lerer Hippeau Ventures and will be earmarked for improvements to Poncho's natural language processing, in addition to building artificial intelligence and data science technology into its bots and apps. Participating investors include Greycroft Partners, Comcast Ventures LP, Venture51 Capital Partners, RRE Ventures, Betaworks, Broadway Video Ventures, Ore Ventures, and several angel investors. Started two years ago out of Betaworks, Poncho offers a weather forecast alternative to Yahoo Weather, AccuWeather, and The Weather Channel. The company seeks to dominate what CEO Sam Mandel calls "thin content," which is activity that "takes place within the notification layer and also on a messaging platform that's contextually relevant, customized, and comes at the right time, but with enough polish to be engaging and cause a happy emotion."

Twitter is an extremely popular online social networking and micro-blogging service. Users communicate through "tweets" - these are short 140-character messages or opinions about different topics. This site is a mine of information about users and their interests - their profile, views, attitudes, observations, people they follow on the site, etc. Apart from being used as a channel of communications between family and friends, Twitter is also used for real-time news updates, recommendations and sharing content. Processing all this information will provide marketers and opinion leaders with a wealth of knowledge about consumers and their behavior and enable them to design effective marketing strategies. Join this webinar to learn how to extract, analyse and utilize this data by implementing machine learning algorithm on the available information.

The last few years have seen numerous studies pointing to a bleak future with technology-induced unemployment on the rise. For example, a pivotal 2013 study by researchers at the University of Oxford found that of 702 unique job types in the United States economy, around 47% were at high risk of computerisation. This was backed up by similar findings in Australia suggesting 44% of occupations – representing more than five million jobs – were at risk over the coming 10 to 15 years. Is the situation really so dire? Are we heading towards mass unemployment as computers and robots do all the work?

This past week was the inauguration of Harvard University's Black Hole Initiative. Stephen Hawking gave a lecture, media was gathered, and millions of dollars committed. A mural was also unveiled, full of fantastical dust swirls, particle jets, and an interstellar bottle carrying Einstein's equations. The painter, Robbert Dijkgraaf, happened to know the equations already, from his day job: string theorist at, and director of, the Institute for Advanced Study in Princeton. Albert Einstein, John von Neumann, and Kurt Gödel hung their hats at the storied institution, back in the day. Einstein's grand piano even sits in Dijkgraaf's living room--"just to be able to touch it is magic," he says. Keenly aware of the historical weight of the Institute and his position in it, Dijkgraaf serves both as a physicist and as a public figure. Painting isn't his only extracurricular: A former president of the Royal Netherlands Academy of Arts and Sciences, he is a regular fixture on Dutch television, and is deeply interested in science education, policy, and outreach. He sat down with Nautilus on the campus of the Institute this April. The video interview plays at the top of the screen. The honeycombs in which they store their amber nectar are marvels of precision engineering, an array of prism-shaped cells with a perfectly hexagonal cross-section. The wax walls are made with a very precise thickness, the...READ MORE If nature had a human personality, what would it be? I think it's part of being a scientist to understand the character of nature. For instance, even if you're a theoretical physicist, you would describe certain mathematical equations to describe natural phenomena. Or, how does nature let herself be captured? And then you just notice that the specific kind of mathematics or the specific kind of reasoning is very effective.

Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep learning to substitute for well-established analysis techniques that rely on hand-crafted feature extraction and classification techniques. From these isolated applications of custom deep architectures it is, however, difficult to gain an overview of their suitability for problems ranging from the recognition of manipulative gestures to the segmentation and identification of physical activities like running or ascending stairs. In this paper we rigorously explore deep, convolutional, and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors. We describe how to train recurrent approaches in this setting, introduce a novel regularisation approach, and illustrate how they outperform the state-of-the-art on a large benchmark dataset. Across thousands of recognition experiments with randomly sampled model configurations we investigate the suitability of each model for different tasks in HAR, explore the impact of hyperparameters using the fANOVA framework, and provide guidelines for the practitioner who wants to apply deep learning in their problem setting.

We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling. The system represents the state-of-the-art in latent variable models for both the ImageNet and Omniglot datasets. We show that it naturally separates global conceptual information from lower level details, thus addressing one of the fundamentally desired properties of unsupervised learning. Furthermore, the possibility of restricting ourselves to storing only global information about an image allows us to achieve high quality 'conceptual compression'.

Sparse generalized eigenvalue problem plays a pivotal role in a large family of high-dimensional learning tasks, including sparse Fisher's discriminant analysis, canonical correlation analysis, and sufficient dimension reduction. However, the theory of sparse generalized eigenvalue problem remains largely unexplored. In this paper, we exploit a non-convex optimization perspective to study this problem. In particular, we propose the truncated Rayleigh flow method (Rifle) to estimate the leading generalized eigenvector and show that it converges linearly to a solution with the optimal statistical rate of convergence. Our theory involves two key ingredients: (i) a new analysis of the gradient descent method on non-convex objective functions, as well as (ii) a fine-grained characterization of the evolution of sparsity patterns along the solution path. Thorough numerical studies are provided to back up our theory. Finally, we apply our proposed method in the context of sparse sufficient dimension reduction to two gene expression data sets.