R is a data analysis software as well as a programming language. Data scientists, statisticians and analysts use R for statistical analysis, data visualization and predictive modeling. R is open source and allows integration with other applications and systems. Compared to other data analysis platforms, R has an extensive set of data products. Problems faced with data are cleared with R's excellent data visualization feature.

My name is Kamal thakur, I am an Electronics Engineer and electronic hobbyist with an interest in making embedded systems, Robotics understandable and enjoyable to other enthusiasts of all experience and knowledge levels. Experienced with project design, development & commissioning, product & application technical support, training & consulting services with international environment. Always eager to learn, I invested a lot of time in learning and teaching, covering a wide range of different scientific topics. Being an electronics engineer, Today I am passionate about data science, artificial intelligence and deep learning for Robotics. I will do my very best to convey my passion for data science to you.

The company's Making and Science Lab, a department dedicated to supporting science and engineering education, has recruited 1,300 volunteers to capture the phenomenon, each armed with a high-resolution camera and a telephoto lens. The group–mostly astronomy buffs and amateur photographers–are stationed along the path stretching from Salem, Oregon, to Charleston, South Carolina, where they'll be able to see the total eclipse as well as the corona, or the sun's tenuous atmosphere. This fleet of photographers will send their images to Google, which will use an algorithm it created to stitch the images together into what it calls an "Eclipse Megamovie" showing a time-expanded video of the total solar eclipse as it crosses North America. The idea is to gather a rich data set around the first total solar eclipse to cross a large portion of the United States in almost 100 years. Technology has changed exponentially in the last century; this rare cosmic event is the first time many will experience a total eclipse, and it's also an opportunity to experience it with new technology.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Sadly, not everyone can afford a lab full of robots to experiment with. We'll be checking back in with the Robotarium just as soon as the rest of the world starts using it for research.

Renowned artificial intelligence expert Andrew Ng hopes to raise up to $150 million to fund more work in AI, according to documents filed this week with the U.S. Securities & Exchange Commission. The documents were first spotted by online private capital community site PE Hub. Given his track record, Ng should have no trouble finding funding for this white-hot tech area, which aims to make computers smarter. Ng is the former chief scientist at Chinese tech giant Baidu (bidu) and he helped build the Google Brain project with Jeff Dean. He also co-founded online education firm Coursera and is a professor at Stanford University, as noted by news site TechCrunch.

Transfer learning borrows knowledge from a source domain to facilitate learning in a target domain. Two primary issues to be addressed in transfer learning are what and how to transfer. For a pair of domains, adopting different transfer learning algorithms results in different knowledge transferred between them. To discover the optimal transfer learning algorithm that maximally improves the learning performance in the target domain, researchers have to exhaustively explore all existing transfer learning algorithms, which is computationally intractable. As a trade-off, a sub-optimal algorithm is selected, which requires considerable expertise in an ad-hoc way. Meanwhile, it is widely accepted in educational psychology that human beings improve transfer learning skills of deciding what to transfer through meta-cognitive reflection on inductive transfer learning practices. Motivated by this, we propose a novel transfer learning framework known as Learning to Transfer (L2T) to automatically determine what and how to transfer are the best by leveraging previous transfer learning experiences. We establish the L2T framework in two stages: 1) we first learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer for a newly arrived pair of domains by optimizing the reflection function. Extensive experiments demonstrate the L2T's superiority over several state-of-the-art transfer learning algorithms and its effectiveness on discovering more transferable knowledge.

Latent space models are effective tools for statistical modeling and exploration of network data. These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing likelihood for a special class of inner-product models while working simultaneously for a wide range of different latent space models, such as distance models, which allow latent vectors to affect edge formation in flexible ways. These fitting methods, especially the one based on projected gradient descent, are fast and scalable to large networks. We obtain their rates of convergence for both inner-product models and beyond. The effectiveness of the modeling approach and fitting algorithms is demonstrated on five real world network datasets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.

We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT) and $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$ (WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically define classes of galaxies in a three-dimensional space spanned by the $\log$ [OIII]/H$\beta$, $\log$ [NII]/H$\alpha$, and $\log$ EW(H${\alpha}$), optical parameters. The best-fit GMM based on several statistical criteria suggests a solution around four Gaussian components (GCs), which are capable to explain up to 97 per cent of the data variance. Using elements of information theory, we compare each GC to their respective astronomical counterpart. GC1 and GC4 are associated with star-forming galaxies, suggesting the need to define a new starburst subgroup. GC2 is associated with BPT's Active Galaxy Nuclei (AGN) class and WHAN's weak AGN class. GC3 is associated with BPT's composite class and WHAN's strong AGN class. Conversely, there is no statistical evidence -- based on four GCs -- for the existence of a Seyfert/LINER dichotomy in our sample. Notwithstanding, the inclusion of an additional GC5 unravels it. The GC5 appears associated to the LINER and Passive galaxies on the BPT and WHAN diagrams respectively. Subtleties aside, we demonstrate the potential of our methodology to recover/unravel different objects inside the wilderness of astronomical datasets, without lacking the ability to convey physically interpretable results. The probabilistic classifications from the GMM analysis are publicly available within the COINtoolbox (https://cointoolbox.github.io/GMM\_Catalogue/).

About this course: This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles. In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true.

The use of data today is becoming more common, yet, businesses are still learning the ropes of addressing their – in some cases huge – data lakes. Adding to that, is the penetration of artificial intelligence (AI) capabilities into the mix of tools used in the data analytics process. Machine learning, deep dreaming and deep learning are just some of the most outstanding AI today. However, there is still a long way to go until these make it into the wider enterprise layer. So, says Ali Ghodsi, co-founder and CEO of Databricks, an open source data software startup.