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Statistics, Data Mining, and Machine Learning in Astronomy - ACAT 2017
 
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Statistics, Data Mining, and Machine Learning in Astronomy Prof. Jacob Vanderplas Wednesday - 08/23/2017
Views: 227 ACAT 2017
AstroML: data mining and machine learning for Astronomy
 
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Jake Vanderplas, Zeljko Ivezic, Andrew Connolly, Alex Gray
Views: 3322 Next Day Video
Statistics Applied to Astronomy: Have You Ever Heard of It?
 
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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
What is ASTROSTATISTICS? What does ASTROSTATISTICS mean? ASTROSTATISTICS meaning & explanation
 
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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: 33 The Audiopedia
Looking for exoplanets using supervised machine learning
 
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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: 228 Data Institute
Statistics and the Astronomical Enterprise 2016
 
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presented by Dr. Eric Feigelson (Penn State)
Data-Driven Astronomical Inference with Machine Learning
 
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Joshua Bloom University of California, Berkeley March 21, 2014
Views: 287 UC-HiPACC
Dr Caroline Clark - A beginner's guide to data analysis in cosmology using Jupyter Notebook
 
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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: 1379 PyData
Opening Up Astronomy with Python and AstroML; SciPy 2013 Presentation
 
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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: 2889 Enthought
Statistics for Data Science and Machine Learning in 8 minutes
 
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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: 44 DataTrained
Machine Learning for Time-Domain Astrophysics
 
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Josh Bloom, Astronomy, UC Berkeley https://simons.berkeley.edu/talks/josh-bloom-2-26-18 Applications in the Natural Sciences and Physical Systems
Views: 332 Simons Institute
Jake VanderPlas: Introduction to Machine Learning in Astronomy with scikit-learn in python
 
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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: 215 ESAC Data Analysis
Machine Learning in Physics
 
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▶ Topics ◀ Machine Learning, Convolutional Neural Networks, Reinforcement Learning ▶ Social Media ◀ [Instagram] @prettymuchvideo ▶ Music ◀ TheFatRat - Fly Away feat. Anjulie https://open.spotify.com/track/1DfFHyrenAJbqsLcpRiOD9
Views: 1724 Pretty Much Physics
Data Mining, Machine Learning, Data Science
 
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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
Risa Wechsler: Cosmologists use data science to explain the origins of the universe
 
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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.”
Jake VanderPlas: Basic Principles of Machine Learning for Astronomy with python
 
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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: 523 ESAC Data Analysis
Machine Learning and Cosmology: New Approaches to Constraining the Dark Universe
 
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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: 663 NanoBio Node
Jake VanderPlas: Introduction to Machine Learning for Astronomy in python
 
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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: 823 ESAC Data Analysis
George Djorgovski - CS+Astronomy - Alumni College 2016
 
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"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: 1363 caltech
What is ASTROINFORMATICS? What does ASTROINFORMATICS mean? ASTROINFORMATICS meaning
 
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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: 107 The Audiopedia
#81: Python and Machine Learning in Astronomy -- Talk Python To Me
 
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The advances in Astronomy over the past century are both evidence of and confirmation of the highest heights of human ingenuity. We have learned by studying the frequency of light that the universe is expanding. By observing the orbit of Mercury that Einstein's theory of general relativity is correct. It probably won't surprise you to learn that Python and data science play a central role in modern day Astronomy. This week you'll meet Jake VanderPlas, an astrophysicist and data scientist from University of Washington. Join Jake and me while we discuss the state of Python in Astronomy. Full show notes at https://talkpython.fm/episodes/show/81/python-and-machine-learning-in-astronomy
Views: 162 Talk Python
Difference between data mining and machine learning
 
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Difference between data mining and machine learning
Views: 1979 Nisha Singh
A Machine That Can Predict Exoplanets
 
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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: 1092 Cool Worlds
Python for astronomical data analysis - Lecture 1/9
 
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Lecture 1 of a course on astronomical data analysis using Python. This lecture is titled "Why Python?"
Views: 1911 Yogesh Wadadekar
Machine Learning
 
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Machine Learning: An overview with the help of R software Preface This book intends to provide an overview of Machine Learning and its algorithms & models with help of R software. Machine learning forms the basis for Artificial Intelligence which will play a crucial role in day to day life of human beings in the near future. A basic understanding of machine learning is required, as its application is widely seen in different fields such as banks and financial sectors, manufacturing, aviation, transportation and medical field. The book covers machine learning classification algorithms such as K-Nearest Neighborhood, Naïve Bayes, Decision Trees and also Artificial Neural Networks and Support Vector Machines. It is recommended to refer author’s book on Application of Statistical Tools in Biomedical Domain: An Overview with Help of Software (https://www.amazon.com/dp/1986988554) and Essentials of Bio-Statistics: An overview with the help of Software https://www.amazon.com/dp/B07GRBXX7D if you need to familiarize yourself with the basic statistical knowledge. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php Amazon link https://www.amazon.com/dp/1790122627 (Paper Back) https://www.amazon.com/dp/B07KQSN447 (Kindle Edition)
Views: 102 editor ijsmi
Data Science in 30 Minutes: Kirk Borne - A Fortuitous Career in Data Science
 
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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: 2277 The Data Incubator
Machine learning with Python and sklearn - Hierarchical Clustering (E-commerce dataset example)
 
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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: 2967 Vytautas Bielinskas
CITA 808: Craters, Planets and Redshifts: Three applications of Machine Learning in Astrophysics
 
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Title: Craters, Planets and Redshifts: Three applications of Machine Learning in Astrophysics Speaker: Kristen Menou (University of Toronto) Date: 2018-02-08
Views: 138 CITA Presentations
Introduction to the basics of astronomy data access
 
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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: 247 Luisa Rebull
Machine Learning Meets Economics: Using Theory, Data, and Experiments to Design Markets
 
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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: 12364 UW Video
Lecture 9: A Quick Tour of Machine-Learning and Statistical Tools
 
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NYU-CCPP 2013 Astro Statistics Seminar Series Lecture 9 Date: 19 April 2013 Lecturer: David Hogg
Asteroids tour from the second Gaia data release. Credit: Institute of Astronomy
 
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Asteroids tour from the second Gaia data release. Credit: Institute of Astronomy
Views: 28 Paul Brackley
Astrophysics Data System Tutorial
 
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This is a short video on how to get started with using the Astrophysics Data System (ADS) https://ui.adsabs.harvard.edu/ ADS is a database operated by the Smithsonian Astrophysical Observatory and funded by NASA. It contains over 12 million records of astronomy and astrophysics articles, including all issues of ‘Monthly Notices of the Royal Astronomical Society’ from 1827 to the present day. Much more guidance on using this excellent database is available on the ADS help pages: http://adsabs.github.io/help/
Data-Driven Astronomy in the 2020s and Beyond – Leah Fulmer
 
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THIS MONTH we will be joined by Leah Fulmer*, who will speak about her research on Data-Driven Astronomy in the 2020s and Beyond. This month we are excited to again be hosted by Peddler Brewing Company in their large beer garden. Each FREE Astronomy on Tap event features accessible, engaging science presentations on topics ranging from planets to black holes to the beginning of the Universe. Most events have games and prizes to test and reward your new-found knowledge! There is always lots of time to ask questions and interact with the presenters and other scientists who inevitably stick around for the beer. Be sure to follow us on Twitter and Facebook to get updates on future events! Speaker Bio Leah Fulmer Title: Data-Driven Astronomy in the 2020s and Beyond Leah Fulmer is a first-year graduate student at the University of Washington, studying the application of statistics and data science techniques in astronomical research. Previously, she completed her undergraduate studies at the University of Wisconsin-Madison and worked as a Data Reduction Specialist at the National Optical Astronomy Observatory.
Statistics in astronomy and the SAMSI ASTRO Program by G  Jogesh Babu
 
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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
The Data Science Economy - DataEDGE 2015
 
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The Data Science Economy Vijay K. Narayanan Friday, May 8, 2015 http://dataedge.ischool.berkeley.edu/2015/schedule/data-science-economy In this talk, I will present three distinct aspects of the data science economy: 1. data, algorithms, systems and humans as the four main drivers of the data science economy 2. a marketplace of intelligent APIs hosted on the cloud that can be easily consumed to build higher level intelligent applications 3. data enabled applications on the cloud in traditional industries. Vijay K. Narayanan Director, Algorithms and Data Science Solutions Microsoft Vijay K Narayanan leads the Algorithms and Data Science efforts in the Information Management and Machine Learning group in Microsoft, where he works on building and leveraging machine learning platforms, tools and solutions to solve analytic problems in diverse domains. Earlier, he worked as a Principal Scientist at Yahoo! Labs, where he worked on building cloud based machine learning applications in computational advertising, as an Analytic Science Manager in FICO where he worked on launching a product to combat identify theft and application fraud using machine learning, as a Modeling Researcher at ACI Worldwide, and as a Sloan Digital Sky Survey research fellow in Astrophysics at Princeton University where he co-discovered the ionization boundary and the four farthest quasars in the universe. He received a Bachelor of Technology degree from IIT, Chennai and a PhD in Astronomy from The Ohio State University. Narayanan has authored or coauthored approximately 55 peer-reviewed papers in astrophysics, 10 papers in machine learning and data mining techniques and applications, and 15 patents (filed or granted). He is deeply interested in the theoretical, applied, and business aspects of large scale data mining and machine learning, and has indiscriminate interests in statistics, information retrieval, extraction, signal processing, information theory, and large scale computing.
Gaia: Astronomy Data Query Language (ADQL) Introduction
 
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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
NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Machine Learning's Role...
 
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Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Machine Learning's Role in the Search for Fundamental Particles by Daniel Whiteson Daniel Whiteson is an Associate Professor in the Department of Physics & Astronomy at UC Irvine. His research area is experimental particle physics, using data from the world's most powerful colliders to answer questions about the fundamental nature of matter and interactions at the smallest scales. He has a long-standing interest in machine learning and has collaborated with machine learning researchers to apply new ideas to the problems of particle physics. Abstract: High-energy physicists try to decompose matter into its most fundamental pieces by colliding particles at extreme energies. But to extract clues about the structure of matter from these collisions is not a trivial task, due to the incomplete data we can gather regarding the collisions, the subtlety of the signals we seek and the large rate and dimensionality of the data. These challenges are not unique to high energy physics, and there is the potential for great progress in collaboration between high energy physicists and machine learning experts. I will describe the nature of the physics problem, the challenges we face in analyzing the data, the previous successes and failures of some ML techniques, and the open challenges.
Views: 1717 GoogleTechTalks
המועדון האסטרונומי Big data and machine learning in astrophysics נתוני עתק ולמידת מכונה באסטרופיזיקה
 
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lecturer: Prof. Dovi Poznanski מרצה: פרופ' דובי פוזננסקי המועדון האסטרונומי 31.10.18
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