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: 333 ACAT 2017
AstroML: data mining and machine learning for Astronomy
Jake Vanderplas, Zeljko Ivezic, Andrew Connolly, Alex Gray
Views: 3639 Next Day Video
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
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: 409 Luisa Rebull
Statistics and the Astronomical Enterprise 2016
presented by Dr. Eric Feigelson (Penn State)
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: 1688 PyData
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: 341 Data Institute
New medical knowledge can be generated using data mining and machine learning methods on patient data.
Views: 9 Herbert Chase
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: 3055 Enthought
Principles of Data Mining
Learn more at: http://www.springer.com/978-1-4471-7306-9. Presents the principal techniques of data mining with particular emphasis on explaining and motivating the techniques used. Focuses on understanding of the basic algorithms and awareness of their strengths and weaknesses. Does not require a strong mathematical or statistical background. Main Discipline: Computer Science
Views: 244 SpringerVideos
ExoPlanet Detection - Advanced Data Mining Project
Performed preliminary analysis and prepare the data to be fed in to our data mining models. We have used multiple models to efficiently classify the exoplanets. The first model, H2O’s Deep Learning, is based on a multi-layer feedforward ANN. We have then performed the Gradient Boosting technique using XGBoost algorithm, an ensemble technique that works on the concept of Decision Tree and Bootstrap Aggregation. Finally, we have used CNN, a Black Box method, to classify the exoplanets.
Views: 26 Narender Kataria
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: 72 DataTrained
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
Python Tutorial: Exoplanet and Star Data Analysis
Find the code files here: https://github.com/whatdamath/spaceengine Hello and welcome to What Da Math! In this video, we will talk about analysis exoplanet data with Python Links: https://exoplanetarchive.ipac.caltech.edu/ https://exoplanets.nasa.gov https://seaborn.pydata.org/examples/horizontal_boxplot.html Code: %matplotlib inline import numpy as np import seaborn as sns import matplotlib.pyplot as plt import pandas as pd planets = pd.read_csv("planets.csv", sep=',') sns.set(style="ticks") # Initialize the figure with a logarithmic x axis f, ax = plt.subplots(figsize=(7, 6)) ax.set_xscale("log") # Load the example planets dataset #planets = sns.load_dataset("planets") # replace pl_pnum and st_mass with other column names sns.boxplot(x="pl_pnum", y="st_mass", data=planets) # Add in points to show each observation sns.swarmplot(x="pl_pnum", y="st_mass", data=planets, size=2, color=".3", linewidth=0) # Tweak the visual presentation ax.xaxis.grid(True) ax.set(ylabel="") sns.despine(trim=True, left=True) Support this channel on Patreon to help me make this a full time job: https://www.patreon.com/whatdamath Space Engine is available for free here: http://spaceengine.org Enjoy and please subscribe. Twitter: https://twitter.com/WhatDaMath Facebook: https://www.facebook.com/whatdamath Twitch: http://www.twitch.tv/whatdamath Bitcoins to spare? Donate them here to help this channel grow! 1GFiTKxWyEjAjZv4vsNtWTUmL53HgXBuvu
Views: 4973 Anton Petrov
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
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
#81: Python and Machine Learning in Astronomy -- Talk Python To Me
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: 220 Talk Python
Data-Driven Astronomical Inference with Machine Learning
Joshua Bloom University of California, Berkeley March 21, 2014
Views: 296 UC-HiPACC
CITA 808: Craters, Planets and Redshifts: Three applications of Machine Learning in Astrophysics
Title: Craters, Planets and Redshifts: Three applications of Machine Learning in Astrophysics Speaker: Kristen Menou (University of Toronto) Date: 2018-02-08
Views: 154 CITA Presentations
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: 132 The Audiopedia
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
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: 716 NanoBio Node
CS50 2018 Final Project – Exoplanet Detection by Jackson Quick/Zeel Patel
A brief tutorial of our final project for CS50: using machine learning to aid in detecting exoplanets. By Jackson Quick and Zeel Patel, Harvard College Class of 2022.
Views: 79 Jackson Quick
YOW! Data 2016 Natalia Rümmele - Automating Data Integration with Machine Learning #YOWData
The world of data is a messy and unstructured place, making it difficult to gain value from data. Things get worse when the data resides in different sources or systems. Before we can perform any analytics in such a case, we need to combine the sources and build a unified view of the data. To handle this situation, a data scientist would typically go through each data source, identify which data is of interest, and define transformations and mappings which unify these data with other sources. This process usually includes writing lots of scripts with potentially overlapping code – a real headache in the everyday life of a data scientist! In this talk we will discuss how machine learning techniques and semantic modelling can be applied to automate the data integration process. Natalia is a data scientist in the data platforms group at Data61, CSIRO. She is passionate about social network analysis, web mining and machine learning with specialization on data mining and link prediction. Her experience includes working on data intensive projects in Ukraine, Austria, Japan and Australia. For more on YOW! Data, visit http://data.yowconference.com.au
Views: 779 YOW! Conferences
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: 385 Simons Institute
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/
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: 77 The Audiopedia
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.”
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: 1229 Cool Worlds
An Overview of Machine Learning Approaches: Applications to Exoplanet Detection
Watch Prof. Rob Fergus from NYU presenting an overview of machine learning approaches for applications to exoplanet detection at the Keck Institute for Space Studies short course "Mastering the Wave - The Whys and Hows of Exoplanet Imaging" on August 22, 2016.
Views: 513 KISSCaltech
Bringing Data Science into Astronomy
Astronomy is entering a Big Data era, in which traditional methods of identifying and classifying objects are no longer feasible. Dr. Gautham Narayan of the Space Telescope Science Institute talks about his work bringing in new techniques from the world of data science to solve problems in astronomy. NOTE: The Kaggle Competition referred to in the video can be found here: https://www.kaggle.com/c/PLAsTiCC-2018/ Follow Dr. Gautham Narayan on Twitter: https://twitter.com/gsnarayan === Follow Three Body Problems: Twitter: https://twitter.com/3BodyProbs Facebook: https://www.facebook.com/3BodyProbs/ Find the hosts on Twitter: Dr. Rachael Livermore: https://twitter.com/rhaegal Dr. Jeffrey Silverman: https://twitter.com/J_M_Silverman
Views: 762 Three Body Problems
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.
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: 1066 UpX Academy
Exoplanets (discovery, machine classification etc.) , Deep learning in astronomy
Modeling, Machine Learning and Astronomy
Views: 291 Snehanshu S
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
The Data Science Economy - DataEDGE 2015
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.
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
Machine Learning
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: 117 editor ijsmi
Johan Frid: Discovery from the written word
The DATA Theme took place during 2017-2018 at the Pufendorf Institute for Advanced Studies, Lund University, Sweden. In the DATA Theme, we worked to investigate how data can be stored, visualised and how data can be explored to discover patterns, and how it can be used to predict future outcomes. The work of DATA was split into five threads. This meeting concerned the work of Threads three and four: machine learning in astronomy and medicine. The DATA Theme Meeting took place in Lundmarksalen, Astronomy Building, Lund University on Wednesday 23 May, 2018.
Day 2: https://www.youtube.com/watch?v=7cZ1K3Xf6C4 The Python in Astronomy conference at UW http://python-in-astronomy.github.io/2016/
Views: 719 Dan Foreman-Mackey
Chihway Chang – Coding/Decoding the Cosmos – SPS16
Coding/Decoding the Cosmos: Python Applications in Astrophysics "Today, Python tools are used almost everywhere in astrophysics: From modelling the images of stars and galaxies seen in modern large telescopes, to statistical analyses of the data products, to inferring the history of our Universe. I will first give a general overview of the kind of Python packages used in the field and then go in to some specific examples of application that I am involved with. These include mapping the dark matter in the Universe and flying drones to calibrate radio telescopes." Talk recorded at the Swiss Python Summit on February 5th, 2016. Licensed as Creative Commons Attribution 4.0 International.
Views: 1361 Swiss Python Summit