Search results “Statistics data mining and machine learning in astronomy”
Statistics, Data Mining, and Machine Learning in Astronomy - ACAT 2017
Statistics, Data Mining, and Machine Learning in Astronomy Prof. Jacob Vanderplas Wednesday - 08/23/2017
Views: 177 ACAT 2017
AstroML: data mining and machine learning for Astronomy
Jake Vanderplas, Zeljko Ivezic, Andrew Connolly, Alex Gray
Views: 3119 Next Day Video
Computational challenges and opportunities of astronomical big data, Melanie Johnston-Hollitt
Check out all the Strata Data Conference keynotes, sessions, and tutorials here: https://www.safaribooksonline.com/library/view/strata-data-conference/9781491985373/ Melanie Johnston-Hollitt is an internationally prominent radio astronomer, the director of astronomy and astrophysics at Victoria University of Wellington, and CEO of Peripety Scientific Ltd., an astrophysics and data analytics research company based in Wellington, New Zealand. Melanie serves as chair of the board of the $60 million Murchison Widefield Array (MWA) radio telescope and is a founding member of the board of directors of the Square Kilometre Array (SKA) Organisation Ltd., which is tasked with building the world’s largest radio telescope. In her nearly 20-year career, she has been involved in design, construction, and operation of several major radio telescopes, including the Low Frequency Array in the Netherlands, the MWA in Australia, and the SKA, which will be hosted in both Australia and South Africa. These instruments produce massive quantities of data, requiring new and disruptive technologies to allow value to be extracted from the data deluge. As a result, Melanie’s recent interests span the intersection between radio astronomy, signal processing, and big data analytics. She leads a multidisciplinary team in Wellington that is investigating how best to meet the science challenges of these next-generation instruments in the big data era. Subscribe to O'Reilly on YouTube: http://goo.gl/n3QSYi Follow O'Reilly on: Twitter: http://twitter.com/oreillymedia Facebook: http://facebook.com/OReilly Instagram: https://www.instagram.com/oreillymedia LinkedIn: https://www.linkedin.com/company-beta/8459/
Views: 1363 O'Reilly
Data Mining, Machine Learning, Data Science
Quelles applications en épidémiologie et quelles perspectives pour la recherche biomédicale ? Séminaire CESP "méthodologie et épistémologie de la recherche biomédicale" 2015/2016. 23/02/2016
Machine learning with Python and sklearn - Hierarchical Clustering (E-commerce dataset example)
In this Machine Learning & Python video tutorial I demonstrate Hierarchical Clustering method. Hierarchical Clustering is a part of Machine Learning and belongs to Clustering family: - Connectivity-based clustering (hierarchical clustering) - Centroid-based clustering (K-Means Clustering) - https://www.youtube.com/watch?v=iybATqk6LNI - Distribution-based clustering - Density-based clustering In data mining and statistics, Hierarchical Clustering also called hierarchical cluster analysis or HCA is a method of cluster analysis which seeks to build a hierarchy of clusters. In this video I demonstrate how Agglomerative Hierarchical Clustering is working. Must know for Hierarchical Clustering is knowing Dendrograms. Dendrogram helps you to decide the optimal number of clusters for your dataset. For executing task in Python I used: - sklearn library that is for Machine Learning algorithms. - ward method that means Minimum Variance Method. If you are interesting more in Hierarchical Clustering, read my article on LinkedIn where I described my experiment about combining Machine Learning (Hierarchical Clustering) in GIS (Geographical Information System). - https://www.linkedin.com/pulse/machine-learning-gis-hierarchical-clustering-urban-bielinskas Data-set for this example is taken from https://www.kaggle.com. There you can find many dataset for very different Machine Learning tasks. Hierarchicaal Clustering is very usable in solving Data Analysis, Data Mining and Statistics problems. If you have any question or comments please write below. Do not forget to subscribe me if want to follow my new videos about Machine Learning, Data Science, Python programming and relative issues. Follow me on LinkedIn: https://www.linkedin.com/in/bielinskas/
Views: 2489 Vytautas Bielinskas
Statistics Applied to Astronomy: Have You Ever Heard of It?
Find more clients through United Statisticians: www.unitedstatisticians.com In this video, we are going to talk about statistics applied to astronomy, a prominent field of statistics which uses its tools to refine and interpret big databases regarding our universe. Stay on top of the latest news from the field of statistics! Take a look at our blog: www.unitedstatisticians.com/blog Let's get social!! Facebook: fb.me/unitedstatisticians Linkedin: https://www.linkedin.com/company/united-statisticians
Machine Learning Meets Economics: Using Theory, Data, and Experiments to Design Markets
Economists often build "structural models," where they specify a specific model of individual behavior and then use data to estimate the parameters of the model. Although such models require strong assumptions, they have the advantage that they can make principled predictions about what would happen if the environment changed in a way that has not been observed in the past. Stanford University's Susa Athey will describe the application of these techniques to advertiser behavior in sponsored search advertising auctions, focusing on how the models can be used for marketplace design and management. She discusses economists' focus on causal inference in statistical models as well as the ways in which experiments can be used to estimate and test structural models. Also presented are suggestions about research directions at the intersection of economics and machine learning.
Views: 12070 UW Video
Python for astronomical data analysis - Lecture 1/9
Lecture 1 of a course on astronomical data analysis using Python. This lecture is titled "Why Python?"
Views: 1844 Yogesh Wadadekar
Machine Learning vs Statistics
Theoretical differences between machine learning and statistics
Views: 568 Minerva's Data Lab
Astro Hack Week 2016: Machine Learning
Josh Bloom on machine learning
Statistics and the Astronomical Enterprise 2016
presented by Dr. Eric Feigelson (Penn State)
Opening Up Astronomy with Python and AstroML; SciPy 2013 Presentation
Authors: Vanderplas, Jake, University of Washington; Ivezic, Zeljko, University of Washington; Connolly, Andrew, University of Washington Track: General As astronomical data sets grow in size and complexity, automated machine learning and data mining methods are becoming an increasingly fundamental component of research in the field. The astroML project (http://astroML.github.com), first released in fall 2012, provides a common repository for practical examples of the data mining and machine learning tools used and developed by astronomical researchers, written in python. The astroML module offers a host of general data analysis and machine learning routines, loaders for openly-available astronomical datasets, and fast implementations of specific computational methods often used in astronomy and astrophysics. The associated website features hundreds of examples of these routines in action, using real datasets. In this talk I'll go over some of the highlights of the astroML code and examples, and discuss how we've used astroML as an aid for student research, hands-on graduate astronomy curriculum, and the sharing of research tools and results.
Views: 2787 Enthought
Dr Caroline Clark - A beginner's guide to data analysis in cosmology using Jupyter Notebook
Filmed at PyData London 2017 Description Current cosmology experiments face exciting computational challenges in statistics and machine learning, with large amounts of data to process. In this beginner's session, we will use Jupyter Notebook to visualise and analyse real cosmological data, using easy to implement code examples from well known python packages such as AstroML, scipy, healpy, numpy, pymc, scikit-learn and matplotlib. Abstract As cosmology has entered the era of precision measurement, academics face the exciting challenges of analysing large datasets, many of these common to other areas of Big Data. In recent years Python has evolved as a standard tool in astronomy and cosmology due to the availability of open source statistical analysis and machine learning packages that allow the development of robust data analysis pipelines and powerful visualisations. In this session, I’d like to demonstrate how a beginner to the field of cosmology can use open data along with the Jupyter Notebook to visualise and analyse real cosmological data, using easy to implement code examples from well known python packages such as AstroML, scipy, healpy, numpy, pymc, scikit-learn and matplotlib. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1255 PyData
Introduction to the basics of astronomy data access
This talk (which serves audiences in NITARP, Datanauts, and amateur astronomy communities) provides an overview of the basics of astronomy data archives - how to get to it, but first how to understand what you're looking at. The website at the end that collects all the links from the talk is here: http://web.ipac.caltech.edu/staff/rebull/outr/datalinks.html
Views: 166 Luisa Rebull
Machine Learning for Time-Domain Astrophysics
Josh Bloom, Astronomy, UC Berkeley https://simons.berkeley.edu/talks/josh-bloom-2-26-18 Applications in the Natural Sciences and Physical Systems
Views: 292 Simons Institute
Jake VanderPlas: Introduction to Machine Learning for Astronomy in python
Tutorial by Jake VanderPlas at the ESAC Data Analysis and Statistics Workshop 2014. http://www.cosmos.esa.int/web/esac-science-faculty/esac-statistics-workshop-2014 iPython notebook with the code used in the video tutorial: https://github.com/jakevdp/ESAC-stats-2014
Views: 790 ESAC Data Analysis
Looking for exoplanets using supervised machine learning
Workshop Data Science in the Alps, March 20th 2018 Grenoble Alpes Data Institute @grenobledata Data Science for Earth, Space & Environmental Sciences "Data science in astronomical high-contrast imaging: looking for exoplanets using supervised machine learning" by Carlos Alberto Gomez Gonzalez , IPAG, Université Grenoble Alpes https://data-institute.univ-grenoble-alpes.fr/ #research #datascience #machinelearning #exoplanets
Views: 182 Data Institute
Difference between data mining and machine learning
Difference between data mining and machine learning
Views: 1864 Nisha Singh
Jake VanderPlas: Basic Principles of Machine Learning for Astronomy with python
Lecture by Jake VanderPlas at the ESAC Data Analysis and Statistics Workshop 2014. http://www.cosmos.esa.int/web/esac-science-faculty/esac-statistics-workshop-2014 iPython notebook with the code used in the video tutorial: https://github.com/jakevdp/ESAC-stats-2014
Views: 510 ESAC Data Analysis
Machine Learning and Cosmology: New Approaches to Constraining the Dark Universe
Matias Carrasco Kind is a current astronomy graduate student and a CSE fellow at the University of Illinois at Urbana-Champaign. His current research interests lie in cosmology and extragalactic astronomy, especially in large scale structure, galaxy formation and evolution, computational and theoretical cosmology, environmental dependence of galaxy properties, photometric redshift estimation, machine learning techniques and data mining.
Views: 637 NanoBio Node
Jake VanderPlas: Introduction to Machine Learning in Astronomy with scikit-learn in python
Tutorial by Jake VanderPlas at the ESAC Data Analysis and Statistics Workshop 2014. http://www.cosmos.esa.int/web/esac-science-faculty/esac-statistics-workshop-2014 iPython notebook with the code used in the video tutorial: https://github.com/jakevdp/ESAC-stats-2014
Views: 206 ESAC Data Analysis
Data-Driven Astronomical Inference with Machine Learning
Joshua Bloom University of California, Berkeley March 21, 2014
Views: 284 UC-HiPACC
[EN] ReportPortal + Machine Learning details
Full and long version of presentation. Contains 2 main parts: ・ReportPortal (idea, showcase, benefits) ・Machine Learning (kNN) algorithm explanation 2:00 ReportPortal 5:20 Ideas behind RP 7:40 Showcase 23:05 Benefits 28:15 Smart Analysis - Machine Learning 31:20 ML - basis 51:00 Implementation details 1:01:00 Questions
What is ASTROINFORMATICS? What does ASTROINFORMATICS mean? ASTROINFORMATICS meaning - ASTROINFORMATICS definition - ASTROINFORMATICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Astroinformatics is an interdisciplinary field of study involving the combination of astronomy, data science, informatics, and information/communications technologies. Astroinformatics is primarily focused on developing the tools, methods, and applications of computational science, data science, and statistics for research and education in data-oriented astronomy. Early efforts in this direction included data discovery, metadata standards development, data modeling, astronomical data dictionary development, data access, information retrieval, data integration, and data mining in the astronomical Virtual Observatory initiatives. Further development of the field, along with astronomy community endorsement, was presented to the National Research Council (United States) in 2009 in the Astroinformatics "State of the Profession" Position Paper for the 2010 Astronomy and Astrophysics Decadal Survey. That position paper provided the basis for the subsequent more detailed exposition of the field in the Informatics Journal paper Astroinformatics: Data-Oriented Astronomy Research and Education. Astroinformatics as a distinct field of research was inspired by work in the fields of Bioinformatics and Geoinformatics, and through the eScience work of Jim Gray (computer scientist) at Microsoft Research, whose legacy was remembered and continued through the Jim Gray eScience Awards. Though the primary focus of Astroinformatics is on the large worldwide distributed collection of digital astronomical databases, image archives, and research tools, the field recognizes the importance of legacy data sets as well—using modern technologies to preserve and analyze historical astronomical observations. Some Astroinformatics practitioners help to digitize historical and recent astronomical observations and images in a large database for efficient retrieval through web-based interfaces. Another aim is to help develop new methods and software for astronomers, as well as to help facilitate the process and analysis of the rapidly growing amount of data in the field of astronomy. Astroinformatics is described as the Fourth Paradigm of astronomical research. There are many research areas involved with astroinformatics, such as data mining, machine learning, statistics, visualization, scientific data management, and semantic science. Data mining and machine learning play significant roles in Astroinformatics as a scientific research discipline due to their focus on "knowledge discovery from data" (KDD) and "learning from data". The amount of data collected from astronomical sky surveys has grown from gigabytes to terabytes throughout the past decade and is predicted to grow in the next decade into hundreds of petabytes with the Large Synoptic Survey Telescope and into the exabytes with the Square Kilometre Array. This plethora of new data both enables and challenges effective astronomical research. Therefore, new approaches are required. In part, due to this data-driven science is becoming a recognized academic discipline. Consequently, astronomy (and other scientific disciplines) are developing sub-disciplines information and data intensive to an extent that these sub-disciplines are now becoming (or have already become) stand alone research disciplines and full-fledged academic programs. While many institutes of education do not boast an astroinformatics program, the most likely will in the near future. Informatics has been recently defined as "the use of digital data, information, and related services for research and knowledge generation". However the usual, or commonly used definition is "informatics is the discipline of organizing, accessing, integrating, and mining data from multiple sources for discovery and decision support."....
Views: 91 The Audiopedia
A Machine That Can Predict Exoplanets
Exciting new research from the Cool Worlds Lab has delved into the use of machine learning and artificial neural networks for astronomy. In new work from David Kipping & Chris Lam here at Columbia, we've shown how a machine can predict the presence of extra planets in known planetary systems using just a few pieces of information about the system. Chris Lam gives a neural network 101 and explains our implementation works. ::More about this Video:: ► Kipping & Lam 2016, "Transit Clairvoyance: Enhancing TESS follow-up using artificial neural networks": https://arxiv.org/abs/1611.04904 ► Tamayo et al. (2016), "A Machine Learns to Predict the Stability of Tightly Packed Planetary Systems": https://arxiv.org/abs/1610.05359 ► Graff et al. (2013), "SKYNET: an efficient and robust neural network training tool for machine learning in astronomy": https://arxiv.org/abs/1309.0790 ► Waldmann (2016), "Dreaming of atmospheres": https://arxiv.org/abs/1511.08339 ► Outro music by Taylor Davis: https://www.youtube.com/watch?v=dl9kI1yQKZk ::Playlists For Channel:: Latest Cool Worlds Videos ► http://bit.ly/NewCoolWorlds Cool Worlds Research ► http://bit.ly/CoolWorldsResearch Guest Videos ► http://bit.ly/CoolWorldsGuests Q&A Videos ►http://bit.ly/CoolWorldsQA Science of TV/Film ► http://bit.ly/ScienceMovies ::Follow us:: SUBSCRIBE to the channel http://bit.ly/CoolWorldsSubscribe Cool Worlds Lab http://coolworlds.astro.columbia.edu Twitter https://twitter.com/david_kipping Instagram https://www.instagram.com/cool.worlds THANKS FOR WATCHING!!
Views: 1049 Cool Worlds
MasterClass - Machine Learning Tutorial | Part 2
UpX Academy is an exclusive ed-tech venture under the umbrella of Tech Mahindra. We provide live, online and interactive courses on big data and Data Science.
Views: 1030 UpX Academy
Risa Wechsler: Cosmologists use data science to explain the origins of the universe
A new generation of powerful telescopes will generate vast amounts of data, but crunching those numbers poses difficult challenges for data scientists. Cosmology, the study of the origin of the universe, is usually thought of as the realm of astronomers armed with giant telescopes and sophisticated satellites. But it is also a data science, “and a very interesting one,” says Risa Wechsler, an associate professor of physics at Stanford University. Explaining that relationship was the theme of Wechsler’s talk at this year’s Women in Data Science conference at Stanford. “I have the pleasure to talk to you about trying to understand the data of literally the entire universe. So that’s definitely a big data problem.” Cosmology, she says, is striving to answer fundamental questions about the universe, such as how did it begin, what is it made of, what’s accelerating it, and how did galaxies form? The links between cosmology and data science are especially pertinent now as researchers prepare to analyze a flood of data from two new and extremely powerful telescopes – the Large Synoptic Survey Telescope, or LSST, and the Hyper Suprime-Cam Telescope. When completed, the LSST will contain a 3200-megapixel camera, the largest ever built. Cosmology, Wechsler says, has undergone a revolution in the last 20 years, driven by the vast increase in data available to analyze. The LSST, for example, will take a picture of the entire sky every three days, producing about 20 terabytes of data a night. Simulations of the universe have grown exponentially over the years. Wechsler demonstrated that by showing a simulation made in 1985, which contained 32,000 particles. A similar simulation performed in 2014 contained a trillion particles. Building it took several tens of millions of CPU hours on one of the largest computers in the world, according to Wechsler. Building the models involves combining data from parts of the visible universe with data derived using machine learning and other types of statistical inference. Doing so allows scientists to make predictions about what the universe as a whole looks like and how to infer the parameters that determine how it behaves. Dealing with such a massive amount of data poses challenges. Often the available computational resources aren’t sufficient, “so we have to be smart about picking which pieces of that problem we solve and in what combination,” Wechsler says. Accuracy is important to all researchers, of course, but Wechsler says she and her colleagues feel a special responsibility to be careful. “We’re really trying to tell you what is the physics of the universe, and we don’t want to get it wrong.”
George Djorgovski - CS+Astronomy - Alumni College 2016
"Exploring Space in Cyberspace" George Djorgovski, Professor of Astronomy, Executive Officer for Astronomy, and Director of the Center for Data Driven Discovery, is an astrophysicist whose work encompassed a broad range of topics, from globular star clusters to the forming galaxies, quasars, supermassive black holes, and their evolution. He was one of the founders of the Virtual Observatory framework, the principal investigator of three digital sky surveys, and is currently working on the establishment of astroinformatics, a discipline bridging astronomy and applied computer science and information technology. His principal scientific focus has gradually shifted toward the ways in which information and computation technologies are changing the ways we do science and scholarship in general, and the emergence of a new scientific methodology for the computationally enabled, data rich science in the 21st century. The Caltech Alumni Association held a day-long event to explore the ways in which computational thinking at Caltech is disrupting science and engineering, creating entirely new disciplines with "CS+X". From developing new paradigms for computation—quantum computing and DNA computing—to pushing the boundaries of machine learning and statistics in ways that transform fields like astronomy, chemistry, neuroscience, and biology, Caltech faculty are pioneering new disciplines at the interface of computer science, and science and engineering. Learn more about the event - http://alumni.caltech.edu/alumni-college Produced in association with Caltech Academic Media Technologies. ©2016 California Institute of Technology
Views: 1204 caltech
Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners
In this Data Mining Example in Statistics Using Python Course, we clean Tuberculosis Data from Headley Article. We use pandas in Jupyter lab to perform exploratory data analysis In this Python data Science course. this is a short data cleaning example for python data science learners. 🔷🔷🔷🔷🔷🔷🔷 Jupyter notebooks and Data sets for Practice : https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷 Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI 🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g
Views: 381 TheEngineeringWorld
Gaia: Astronomy Data Query Language (ADQL) Introduction
Hendrik Heinl / Stefan Jordan - ARI Heidelberg Presentation recorded during the first Gaia data workshop at ESA's European Space Astronomy Centre (ESAC) 2-4 November 2016. The slides to this presentation are available here: http://www.cosmos.esa.int/documents/915837/915858/plenary_presentation.pdf
Lecture 9: A Quick Tour of Machine-Learning and Statistical Tools
NYU-CCPP 2013 Astro Statistics Seminar Series Lecture 9 Date: 19 April 2013 Lecturer: David Hogg
2.1.2 Parametric vs. Non-Parametric Statistical Learning
Book: Introduction to Statistical Learning - with Applications in R http://www-bcf.usc.edu/~gareth/ISL/
Views: 994 MachineLearningGod
From Data to Knowledge - 510 - Joseph Richards
Joseph Richards: "Classification of Astronomical Time Series in the Synoptic Survey Era". A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012). Abstract Joey Richards (UC Berkeley, Astronomy & Statistics) We have entered the Synoptic Survey Era of observational astronomy, where data rates are quickly reaching several terabytes per night. A growing army of telescopes monitor, nightly, the brightnesses of millions, and soon to be upwards of a billion objects. Real-time analysis of these data is critical to determine which objects and events require timely observations with expensive follow-up resources. To maximize the scientific returns from these massive projects, sophisticated machine-learning tools must be used. Our group has been on the cutting edge of the methodological and algorithmic development for time-domain astronomical data analysis. I will describe several problems in which we have made great strides, including real-time ML classification of transient events, photometric supernova typing, and probabilistic classification of variable stars from long-baseline time series. We address a multitude of statistical issues, and in this talk I will describe our use of manifold learning for feature extraction in time series, active learning to overcome sample-selection biases, and semi-supervised learning to detect anomalies in data streams. I will describe the newly released Machine-learned ASAS Classification Catalog (MACC, www.bigmacc.info) and discuss the future of astronomical source catalogs.
Views: 655 ckleinastro
AstroStatistics: What is it good for?
Aneta Siemiginowska (CfA) and Vinay Kashyap (CfA) Introductory talk on AstroStat for CHASC, 2014-sep-02 We introduce astrostatistics concepts to Statistics students. We describe the nature of high-energy X-ray and gamma-ray data and walk through some examples of high-energy astronomical sources. We then review the work done by CHASC graduate students. http://hea-www.harvard.edu/AstroStat/Stat310_1415/#asvk_20140902
Views: 623 Vinay Kashyap
What is ASTROSTATISTICS? What does ASTROSTATISTICS mean? ASTROSTATISTICS meaning & explanation
What is ASTROSTATISTICS? What does ASTROSTATISTICS mean? ASTROSTATISTICS meaning . ASTROSTATISTICS pronunciation - ASTROSTATISTICS definition - ASTROSTATISTICS explanation - How to pronounce ASTROSTATISTICS? SUBSCRIBE to our Google Earth flights channel - http://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ?sub_confirmation=1 Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Astrostatistics is a discipline which spans astrophysics, statistical analysis and data mining. It is used to process the vast amount of data produced by automated scanning of the cosmos, to characterize complex datasets, and to link astronomical data to astrophysical theory. Many branches of statistics are involved in astronomical analysis including nonparametrics, multivariate regression and multivariate classification, time series analysis, and especially Bayesian inference. Practitioners are represented by the International Astrostatistics Association affiliated with the International Statistical Institute, the International Astronomical Union Working Group in Astrostatistics and Astroinformatics, the American Astronomical Society Working Group in Astroinformatics and Astrostatistics, and the American Statistical Association Interest Group in Astrostatistics. All of these organizations participate in the Astrostatistics and Astroinformatics Portal Web site.
Views: 20 The Audiopedia
Deep Learning for Science
In this video from the 2017 Intel HPC Developer Conference, Prabhat from NERSC and Michael F. Wehner from LBNL present: Deep Learning for Science. "Deep Learning has revolutionized the fields of computer vision, speech recognition and control systems. Can Deep Learning (DL) work for scientific problems? This talk will explore a variety of Lawrence Berkeley National Laboratory’s applications that are currently benefiting from DL. We will review classification and regression problems in astronomy, cosmology, neuroscience, genomics and high-energy physics. We will share results from a deep-dive into the problem of detecting extreme weather patterns in climate simulations. Lastly, we will conclude with short and long-term challenges at the frontier of DL research, and speculate about the role of DL and AI in the future of scientific discovery." Prabhat leads the Data and Analytics Services team at NERSC. In this role, he is responsible for deploying the Big Data stack on NERSC platforms, spanning capability areas in Data Analytics, Management, Workflows, Visualization, Transfer and Access. Prabhat is the Director of the Big Data Center at NERSC, which is enabling capability Data applications to run on the Cori supercomputer. Prabhat’s current research interests span Deep Learning, Machine Learning and Applied Statistics. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley. Michael F. Wehner is a senior staff scientist in the Computational Research Division at the Lawrence Berkeley National Laboratory. Dr. Wehner’s current research concerns the behavior of extreme weather events in a changing climate, especially heat waves, intense precipitation, drought and tropical cyclones. Before joining the Berkeley Lab in 2002, Wehner was an analyst at the Lawrence Livermore National Laboratory in the Program for Climate Modeling Diagnosis and Intercomparison. He is the author or co-author of over 165 scientific papers and reports. He was a lead author for both the 2013 Fifth Assessment Report of the Intergovernmental Panel on Climate Change and the 2nd,3rd and 4th US National Assessments on climate change. Dr. Wehner earned his master’s degree and Ph.D. in nuclear engineering from the University of Wisconsin-Madison, and his bachelor’s degree in Physics from the University of Delaware. Learn more: https://www.intel.com/content/www/us/en/events/hpcdevcon/overview.html Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Views: 459 RichReport
Yisong Yue - CS+Data - Alumni College 2016
"Automatically Improving Automation Using Big Data" Yisong Yue, Assistant Professor of Computing and Mathematical Sciences, is director of Decision, Optimization, and Learning at the California Institute of Technology (DOLCIT), which brings together experts in machine learning, optimization, applied math, statistics, control, robotics, and human-computer interaction. Its goal is to work on creating a world where intelligent systems seamlessly integrate learning and planning, as well as automatically balance computational and statistical tradeoffs in the underlying optimization problems. Yue was previously a research scientist at Disney Research and before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. The Caltech Alumni Association held a day-long event to explore the ways in which computational thinking at Caltech is disrupting science and engineering, creating entirely new disciplines with "CS+X". From developing new paradigms for computation—quantum computing and DNA computing—to pushing the boundaries of machine learning and statistics in ways that transform fields like astronomy, chemistry, neuroscience, and biology, Caltech faculty are pioneering new disciplines at the interface of computer science, and science and engineering. Learn more about the event - http://alumni.caltech.edu/alumni-college Produced in association with Caltech Academic Media Technologies. ©2016 California Institute of Technology
Views: 3074 caltech
Asteroids tour from the second Gaia data release. Credit: Institute of Astronomy
Asteroids tour from the second Gaia data release. Credit: Institute of Astronomy
Views: 25 Paul Brackley
Data Science in 30 Minutes: Kirk Borne - A Fortuitous Career in Data Science
In this webinar from August of 2017, renowned data scientist Kirk Borne took viewers on a journey through his career in science and technology and explained how the industry-and himself have evolved over the last 4 decades. Starting with skipping lunches in high school to a systematic twitter obsession, Kirk shed light on his road to success in the data science industry. The Data Incubator is a data science education company based in NYC, DC, and SF with both corporate training as well as recruiting services. For data science corporate training, we offer customized, in-house corporate training solutions in data and analytics. For data science hiring, we run a free 8 week fellowship training PhDs to become data scientists. The fellowship selects 2% of its 2000+ quarterly applicants and is free for Fellows. Hiring companies (including EBay, Capital One, Pfizer) pay a recruiting fee only if they successfully hire. You can read about us on Harvard Business Review, VentureBeat, or The Next Web, or read about our alumni at LinkedIn, Palantir or the NYTimes. http://thedataincubator.com About the speakers: Dr. Kirk Borne is a data scientist and an astrophysicist. He is Principal Data Scientist in the Strategic Innovation Group at Booz-Allen Hamilton since 2015. He was Professor of Astrophysics and Computational Science in the George Mason University (GMU) School of Physics, Astronomy, and Computational Sciences during 2003-2015. He served as undergraduate advisor for the GMU Data Science program and graduate advisor to students in the Computational Science and Informatics PhD program. Prior to that, he spent nearly 20 years supporting NASA projects, including NASA's Hubble Space Telescope as Data Archive Project Scientist, NASA's Astronomy Data Center, and NASA's Space Science Data Operations Office. He has extensive experience in large scientific databases and information systems, including expertise in scientific data mining. He was a contributor to the design and development of the new Large Synoptic Survey Telescope (LSST), for which he contributed in the areas of science data management, informatics and statistical science research, galaxies research, and education and public outreach. Michael Li founded The Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to Fellows, employers engage with the Incubator as hiring partners. Previously, he worked as a data scientist (Foursquare), Wall Street quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He completed his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall Scholar. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup to focus on what he really loves. Michael lives in New York, where he enjoys the Opera, rock climbing, and attending geeky data science events.
Views: 2178 The Data Incubator
Dr Jim Geach: Machine Learning Algorithms
Dr Jim Geach, Royal Society Research Fellow in the Centre for Astrophysics Research at the University of Hertfordshire, talks about the relevance of research on the evolution of the Galaxy to contemporary developments such as driverless cars and finding effective treatments for cancer.
Statistics in astronomy and the SAMSI ASTRO Program by G  Jogesh Babu
20 March 2017 to 25 March 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru This joint program is co-sponsored by ICTS and SAMSI (as part of the SAMSI yearlong program on Astronomy; ASTRO). The primary goal of this program is to further enrich the international collaboration in the area of synoptic time domain surveys and time series analysis of gravitational wave data. The participants will focus on advancing the current understanding of these research topics by incorporating expertise of researchers from India and US who are working on identifying electromagnetic counterparts to gravitational wave sources. In essence, this program would enable US researchers to learn from the expertise of Indian researchers and also enable US researchers to exchange and share the methodologies developed by two of the five working groups of the SAMSI ASTRO program. The program will begin with a few overview lectures designed to familiarize attendees with current trends in time domain astronomy and modern methodologies in statistics and applied mathematics. The subsequent part of the program will follow with specialized research lectures on the existing subgroups, panel discussions about collaboration possibilities between different groups with specific end-points in mind through collaborative research sessions. Participation in this program is by invitation only and will involve about 35-40 participants only. If you are interested to participate, please contact one of the organizers. CONTACT US: [email protected] PROGRAM LINK: https://www.icts.res.in/program/TASSGW2017
Statistics for Data Science and Machine Learning in 8 minutes
We have tried to sum up Data Science Statistics in 8 minutes only to give you a fair idea. To add more value and gain expertise, enroll in the DataTrained Full Stack Data Science Certificate program with Job Guarantee or Money Back Challenge
Views: 34 DataTrained

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