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QSS: the Intersection of Data Science and Liberal Arts @ Emory
 
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Traditional Data Science skills (statistics, mathematics, and computing) are increasingly important and essential to most disciplines and careers. As a result, the demand for applied quantitative training with a substantive focus is strong and growing. While most quantitative training at the undergraduate level remains concentrated in math and statistics departments, our interdisciplinary and applied focus is designed to broaden access to those skills. The Institute for Quantitative Theory and Methods promotes the teaching, learning, and use of quantitative analysis across all disciplines. For more information, visit http://www.quantitative.emory.edu
Views: 440 Emory University
Professor Mark Girolami: "Probabilistic Numerical Computation: A New Concept?"
 
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The Turing Lectures: The Intersection of Mathematics, Statistics and Computation - Professor Mark Girolami: "Probabilistic Numerical Computation: A New Concept?" Click the below timestamps to navigate the video. 00:00:09 Introduction by Professor Jared Tanner 00:01:38 Professor Mark Girolami: "Probabilistic Numerical Computation: A New Concept?" 00:54:48 Q&A Lecture blurb: The vast amounts of data in many different forms becoming available to politicians, policy makers, technologists, and scientists of every hue presents tantalising opportunities for making advances never before considered feasible. Yet with these apparent opportunities has come an increase in the complexity of the mathematics required to exploit this data. These sophisticated mathematical representations are much more challenging to analyse, and more and more computationally expensive to evaluate. This is a particularly acute problem for many tasks of interest, such as making predictions since these will require the extensive use of numerical solvers for linear algebra, optimization, integration or differential equations. These methods will tend to be slow, due to the complexity of the models, and this will potentially lead to solutions with high levels of uncertainty. This talk will introduce our contributions to an emerging area of research defining a nexus of applied mathematics, statistical science and computer science, called “probabilistic numerics”. The aim is to consider numerical problems from a statistical viewpoint, and as such provide numerical methods for which numerical error can be quantified and controlled in a probabilistic manner. This philosophy will be illustrated on problems ranging from predictive policing via crime modelling to computer vision, where probabilistic numerical methods provide a rich and essential quantification of the uncertainty associated with such models and their computation. Bio After graduation from the University of Glasgow, Mark Girolami spent the first ten years of his career with IBM as an Engineer. After this he undertook, on a part time basis, a PhD in Statistical Signal Processing whilst working in a Scottish Technical College. He then went on rapidly to hold senior professorial positions at the University of Glasgow, and University College London. He is an EPSRC Established Career Research Fellow (2012 - 2017) and previously an EPSRC Advanced Research Fellow (2007 - 2012). He is the Director of the EPSRC funded Research Network on Computational Statistics and Machine Learning and in 2011, was elected to the Fellowship of the Royal Society of Edinburgh, when he was also awarded a Royal Society Wolfson Research Merit Award. He has been nominated by the Institute of Mathematical Statistics to deliver a Medallion Lecture at the Joint Statistical Meeting in 2017. He is currently one of the founding Executive Directors of the Alan Turing Institute for Data Science His research and that of his group covers the development of advanced novel statistical methodology driven by applications in the life, clinical, physical, chemical, engineering and ecological sciences. He also works closely with industry where he has several patents leading from his work on e.g. activity profiling in telecommunications networks and developing statistical techniques for the machine based identification of counterfeit currency which is now an established technology used in current Automated Teller Machines. At present he works as a consultant for the Global Forecasting Team at Amazon in Seattle. The Alan Turing Institute is the UK's National Institute for Data Science. The Institute’s mission is to: undertake data science research at the intersection of computer science, mathematics, statistics and systems engineering; provide technically informed advice to policy makers on the wider implications of algorithms; enable researchers from industry and academia to work together to undertake research with practical applications; and act as a magnet for leaders in academia and industry from around the world to engage with the UK in data science and its applications. The Institute is headquartered at The British Library, at the heart of London’s knowledge quarter, and brings together leaders in advanced mathematics and computing science from the five founding universities and other partners. Its work is expected to encompass a wide range of scientific disciplines and be relevant to a large number of business sectors. For more information, please visit: https://turing.ac.uk
Running Agile Data Science Teams | Data Dialogs 2015
 
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John Akred, Silicon Valley Data Science http://datadialogs.ischool.berkeley.edu/2015/schedule/running-agile-data-science-teams What’s the best way to pursue data-driven projects? Drawing from our experience with cross-functional teams of engineering, quantitative, and visualization skills, we will highlight the benefits of collaborative teams of experts working iteratively, across disciplines, and explain how to manage these teams to successfully and efficiently deliver data analytics projects. John Akred Founder & CTO Silicon Valley Data Science John Akred is the Founder and CTO of Silicon Valley Data Science. In the business world, John Akred likes to help organizations become more data driven. He has over 15 years of experience in machine learning, predictive modeling, and analytical system architecture. His focus is on the intersection of data science tools and techniques; data transport, processing and storage technologies; and the data management strategy and practices that can unlock data driven capabilities for an organization. A frequent speaker at the O'Reilly Strata Conferences, John is host of the perennially popular workshop: Architecting A Data Platform.
RSS President's address 2015 - Statistics: a Data Science for the 21st Century
 
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Peter Diggle President, Royal Statistical Society The rise of data science could be seen as a potential threat to the long-term status of the statistics discipline. Peter will firstly argue that, although there is a threat, there is also a much greater opportunity to re-emphasise the universal relevance of statistical method to the interpretation of data, and he will give a short historical outline of the increasingly important links between statistics and information technology. He will summarise several recent research projects, through which he hopes to demonstrate that statistics make an essential, but incomplete, contribution to the merging field of 'electronic health' research. Finally, Peter will offer personal thoughts on how statistics might best be organised in a research-led university, on what we should teach our students and on some issues broadly related to data science where the Royal Statistical Society can take a lead.
Views: 2210 RoyalStatSoc
Professor Gareth Roberts: "New challenges in Computational Statistics"
 
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The Turing Lectures: Statistics - Professor Gareth Roberts, University of Warwick “New challenges in Computational Statistics” Click the below timestamps to navigate the video. 00:00:09 Welcome by Professor Patrick Wolfe 00:01:44 Introduction by Professor Sofia Olhede 00:03:23 Professor Gareth Roberts, University of Warwick “New challenges in Computational Statistics” 00:59:59 Q&A The second set of Turing Lectures focuses on Statistical Science and we have two of the world’s leading statistical innovators giving two lectures on the new challenges in computational statistics and its application in life sciences. We will delve into the mysteries of the operation and control of the living cell, seeking to make sense of data obtained from ingenious experiments. Contemporary statistical models required for such complex data is presenting phenomenal challenges to existing algorithms and these talks will present advances being made in this area of Statistical Science. For more information, please visit: https://turing.ac.uk The Alan Turing Institute is the UK's National Institute for Data Science. The Institute’s mission is to: undertake data science research at the intersection of computer science, mathematics, statistics and systems engineering; provide technically informed advice to policy makers on the wider implications of algorithms; enable researchers from industry and academia to work together to undertake research with practical applications; and act as a magnet for leaders in academia and industry from around the world to engage with the UK in data science and its applications. The Institute is headquartered at The British Library, at the heart of London’s knowledge quarter, and brings together leaders in advanced mathematics and computing science from the five founding universities and other partners. Its work is expected to encompass a wide range of scientific disciplines and be relevant to a large number of business sectors. For more information, please visit: https://turing.ac.uk
Professor Luciano Floridi: "Ethics in the Age of Information"
 
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The Turing Lectures: Social Science and Ethics - Professor Luciano Floridi, Oxford Internet Institute, University of Oxford: “Ethics in the Age of Information” Click the below timestamps to navigate the video. 00:00:07 Introduction by Professor Andrew Blake, Director, The Alan Turing Institute 00:02:20 Professor Luciano Floridi, Oxford Internet Institute, University of Oxford: “Ethics in the Age of Information” 00:59:05 Q&A The excitement of Data Science brings the need to consider the ethics associated with the information age. Likewise a revolution in political science is taking place where the internet, social media and real time electronic monitoring has brought about increased mobilisation of political movements. In addition the generation of huge amounts of data from such processes presents on the one hand opportunities to analyse and indeed predict political volatility, and on the other ethical and technical challenges which will be explored by two of the foremost philosophers and political scientists. The Alan Turing Institute is the UK's National Institute for Data Science. The Institute’s mission is to: undertake data science research at the intersection of computer science, mathematics, statistics and systems engineering; provide technically informed advice to policy makers on the wider implications of algorithms; enable researchers from industry and academia to work together to undertake research with practical applications; and act as a magnet for leaders in academia and industry from around the world to engage with the UK in data science and its applications. The Institute is headquartered at The British Library, at the heart of London’s knowledge quarter, and brings together leaders in advanced mathematics and computing science from the five founding universities and other partners. Its work is expected to encompass a wide range of scientific disciplines and be relevant to a large number of business sectors. For more information, please visit: https://turing.ac.uk
Professor Helen Margetts: "The Data Science of Politics"
 
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The Turing Lectures: Social Science and Ethics - Professor Helen Margetts, Director, Oxford Internet Institute, University of Oxford: "The Data Science of Politics" Click the below timestamps to navigate the video. 00:00:07 Introduction by Professor Andrew Blake, Director, The Alan Turing Institute 00:01:40 Professor Helen Margetts, Director, Oxford Internet Institute, University of Oxford: "The Data Science of Politics" 00:50:01 Q&A The excitement of Data Science brings the need to consider the ethics associated with the information age. Likewise a revolution in political science is taking place where the internet, social media and real time electronic monitoring has brought about increased mobilisation of political movements. In addition the generation of huge amounts of data from such processes presents on the one hand opportunities to analyse and indeed predict political volatility, and on the other ethical and technical challenges which will be explored by two of the foremost philosophers and political scientists. The Alan Turing Institute is the UK's National Institute for Data Science. The Institute’s mission is to: undertake data science research at the intersection of computer science, mathematics, statistics and systems engineering; provide technically informed advice to policy makers on the wider implications of algorithms; enable researchers from industry and academia to work together to undertake research with practical applications; and act as a magnet for leaders in academia and industry from around the world to engage with the UK in data science and its applications. The Institute is headquartered at The British Library, at the heart of London’s knowledge quarter, and brings together leaders in advanced mathematics and computing science from the five founding universities and other partners. Its work is expected to encompass a wide range of scientific disciplines and be relevant to a large number of business sectors. For more information, please visit: https://turing.ac.uk
Professor Mark Newman: "Epidemics, Erdos numbers, and the Internet"
 
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The Turing Lectures: Mathematics - Professor Mark Newman: "Epidemics, Erdos numbers, and the Internet" Click the below timestamps to navigate the video. 00:00:07 Lecture introduction by Professor Jared Tanner 00:01:14 Professor Mark Newman: Epidemics, Erdos numbers, and the Internet: The Form and Function of Networks 00:51:02 Q&A The first set of Turing Lectures took place on March 2 2016 with a focus on Mathematics one of the foundations of Data Science. An exciting pair of lectures were delivered by Professors Stéphane Mallat and Mark Newman who considered recent advances in Data Science from a mathematical perspective. Deep Learning and Complex Networks have made the headlines in the scientific and popular press of late, and this Turing Lecture event provided an overview of some of the most recent influential advances in both of these areas. For more information, please visit: https://turing.ac.uk/turing-lectures-... The Alan Turing Institute is the UK's National Institute for Data Science. The Institute’s mission is to: undertake data science research at the intersection of computer science, mathematics, statistics and systems engineering; provide technically informed advice to policy makers on the wider implications of algorithms; enable researchers from industry and academia to work together to undertake research with practical applications; and act as a magnet for leaders in academia and industry from around the world to engage with the UK in data science and its applications. The Institute is headquartered at The British Library, at the heart of London’s knowledge quarter, and brings together leaders in advanced mathematics and computing science from the five founding universities and other partners. Its work is expected to encompass a wide range of scientific disciplines and be relevant to a large number of business sectors. For more information, please visit: https://turing.ac.uk
The Shape of Things to Come | Jeff Murugan | TEDxTableMountain
 
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Jeff believes that the 21st Century will see diverse fields coming together in ways we have never seen before - the dawning of the Era of Information. And by applying the mathematical field of Topology to complex data sets, systems and problems, we can gain fresh insights that could lead to understanding even the most complex entity in the world, the human brain. Jeff Murugan was born in Tongaat. On completion of a BSc degree, majoring in applied mathematics and physics, he obtained a first class Honours degree in specialising in mathematical physics all at UCT. This was followed by an MSc for a thesis on topological solutions of low dimensional field theories. In 2000, he was awarded a Lindbury Fellowship to pursue a PhD jointly at UCT and Oxford University. After being awarded a PhD for his work on noncommutative geometry in string theory, he began work as a postdoctoral fellow at Brown University. He returned to a faculty position as a lecturer at UCT in 2006. Prof. Murugan is currently a deputy head of the Department of Mathematics & Applied Mathematics at UCT. He also heads the Laboratory for Quantum Gravity & Strings. Among his accolades, he is the recipient of the bronze medal of the South African Mathematical Society, a founding member of the South African Young Academy of Science (SAYAS), current president of the SA Gravity Soc. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 2439 TEDx Talks
AIG: Data Science in the Insurance Industry and Financial Services (CXOTalk #259)
 
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Murli Buluswar, former Chief Science Officer at AIG, and currently Senior Executive Advisor the Boston Consulting Group (BCG), speaks with CXOTalk co-hosts Michael Li, CEO of The Data Incubator, and Michael Krigsman about data science and innovation in the insurance industry. How can insurance and financial services companies adapt and thrive in a world of data and digital disruption? For more information, see https://www.cxotalk.com/episode/aig-data-science-insurance-industry Buluswar is a trailblazing and innovative leader. Since 2014, he’s helped AIG evolve from a ‘knowing culture’ to a ‘learning culture,’ from an organization reliant on human judgment to a firm that benefits from its institutional risk insights manifested through data models. Currently, he is a Senior Executive Advisor at the Boston Consulting Group From the transcript: Murli Buluswar: The way I would reframe that is you help them fundamentally recognize that this is not just a separate pillar that you should be thinking of as being incremental to how you will shape your business strategy. These competencies are in the very near future or, in fact, even in the here and now. In effect, a mitochondria that will shape the energy and the life that your firm will have in terms of its sustainability in a world of data and tech driven disruption. The challenge then is that typically in many of these large institutions, you've got leaders who have risen to those senior positions on the basis of historic experiences, which are less relevant if you extrapolate them to the future. And so it really does become an issue around having the humility to develop much more of a learning mindset; and recognizing that the more ambitious you are in terms of really re-sculpting and reshaping your competitive positioning, the more you have to be willing to break glass based on the insights that you achieved through data science. Michael Li: You need a broad swath of the organization to understand the value of data, how you use data--think about some of the issues that Murli and I were just talking about earlier--that really embrace taking time to have their employees learn about data science and big data. On the cultural side, actually, I'd be curious, Murli, to ask you this question. I think one of the things that's maybe unique about insurance or banking is that there is kind of a legacy of data around the actuarials, around the statisticians. How does that change the dynamic of creating a data culture when you have a legacy group that's somewhat already steeped in this? Murli Buluswar: I think there are two parts to that, Michael. One is, how does that change decision-making today, and how should that change decision-making tomorrow? If one were to zoom out, in general I think the actuarial function, the profession, and the exams have not embraced, from my point of view, the power of data science in its totality the way perhaps they should. Maybe they will, looking into the coming few years. The other piece of it is, if you disaggregate the entire value chain of insurance, there's data science that can be applied to many, many, many aspects of it that can fundamentally shape the sophistication, timeliness, [and] granularity of decision-making in ways that the industry could not have imagined a decade ago. To me, the role of data science is very, very widespread, even if one were to dodge the traditional domain of the actuarial sciences. Where I'm hoping the industry is going to head toward is, rather than have this mindset of creating rigid silos or pillars, see that the competencies are interchangeable and they're one in the same. Let's actually move to a world where we're challenging; we understand our assumptions and are challenging those assumptions to shape the caliber, effectiveness, and efficiency of decision-making as opposed to hanging our hats on what titles we've got, what professional credentials we've got, or what academic experiences we have because those are an interesting starting point, but are really not particularly relevant in a world where everything around us is changing at a more profound pace than ever before. Michael Li: With the actuarials, I think that a lot of the really farsighted ones, the ones who are really looking to the future, seem to really understand this and are embracing a lot of these new techniques around data science, around big data, really looking to challenge the assumptions that maybe their own discipline has ingrained into them through indoctrination. [They're] really leveraging the existing knowledge that they have, this really strong knowledge of probability and statistics, and then seeing how they can apply that to the data science, which of course is very rich in probability and stats.
Views: 5049 CXOTALK
Multiple Regression - Dummy variables and interactions - example in Excel
 
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In this video, I present an example of a multiple regression analysis of website visit duration data using both quantitative and qualitative variables. Variables used include gender, browser, mobile/non-mobile, and years of education. Gender and mobile each require a single dummy variable, while browser requires several dummy variables. I also present models that include interactions between the dummy variables and years of education to analyze intercept effects, slope effects, and fully interacted models. In short, I cover: - multiple category qualitative variables - dummy variables - intercept effects - slope effects - dummy interactions I hope you find it useful! Please let me know if you have any questions! --Dr. D.
Views: 230683 Jason Delaney
Lecture 03 -The Linear Model I
 
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The Linear Model I - Linear classification and linear regression. Extending linear models through nonlinear transforms. Lecture 3 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 10, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 236691 caltech
Micro-Biology: Crash Course History of Science #24
 
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It's all about the SUPER TINY in this episode of Crash Course: History of Science. In it, Hank Green talks about germ theory, John Snow (the other one), pasteurization, and why following our senses isn't always the worst idea. *** Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, D.A. Noe, Shawn Arnold, Malcolm Callis, Advait Shinde, William McGraw, Andrei Krishkevich, Rachel Bright, Mayumi Maeda, Kathy & Tim Philip, Jirat, Ian Dundore -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 63489 CrashCourse
Data Issues: Multiple Testing, Bias, Confounding, Missing...
 
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Dr. Lance Waller from Emory University presents a lecture titled "Data Issues: Multiple Testing, Bias, Confounding, & Missing Data." View Slides https://drive.google.com/open?id=0B4IAKVDZz_JUczRSd0NucjlhT00 Lecture Abstract Once data are scraped, wrangled, linked, merged, and analyzed, what information do they reveal and can we trust the resulting conclusions? In this presentation, we define and review data issues relating to the analysis and interpretation of observational data from the field of epidemiology and consider implications for data science, especially regarding the goal of moving from big data to knowledge. Specifically, we explore concepts of bias, confounding, effect modification, and missing/mismeasured data as applied to data science. We provide an analytic context based on sampling concepts and explore relevant literature and tools from epidemiology, biostatistics, computer science, and data science. As with many issues in data science, the full applicability of the concepts is very much a work in progress and present multiple opportunities for future development. About the Speaker Lance A. Waller, Ph.D. is Rollins Professor and Chair of the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He is a member of the National Academy of Science Committee on Applied and Theoretical Statistics. His research involves the development of statistical methods for geographic data including applications in environmental justice, epidemiology, disease surveillance, spatial cluster detection, conservation biology, and disease ecology. His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley). Join our weekly meetings from your computer, tablet or smartphone. Visit our website to view our schedule and join our next live webinar! http://www.bigdatau.org/data-science-seminars
Systems Biology: A Short Overview
 
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Predicting the outcome of an observable phenomenon belongs to the key disciplines of natural sciences. A chemist can precisely calculate the temperature increase when dehydrating sugars upon contact with sulfuric acid. A physicist can predict the force needed to leverage a rock of a certain weight. But for a biologist, the situation is different. It is an excessively difficult and time-consuming task to perform detailed calculations on biological systems. For a long time, it was even believed that a mysterious vital spark drives all living entities. So what makes calculations in biology so different from other sciences? Living entities belong to the most complex systems in existence. At the most basic level, a single cell comprises huge numbers of molecules and is structured in a very densely organized space, All those molecules participate in a numerous biochemical reactions, highly regulated enzymes drive these reactions, and external signals interfere with the cell, in the form of hormones, drugs, or variations in the amount of nutrition available. It is not possible for the human mind to keep track of so many processes in parallel. So, how can we calculate effects of cellular functions? The most viable option is to construct highly detailed computer models that facilitate visualization and statistics to see trends, and mathematical modeling to precisely calculate interactions of components to predict system behavior. In order to be reliable and diagnostically conclusive, these models need to be constricted to real-world conditions by incorporating physicochemical constraints. However, the complexity of the interactions can still be overwhelming. Yet, making biological phenomena predictable is worthwhile. By simulating entire cellular systems we could: Gain a better understanding of the system in its entirety. Calculate how much medicine a patient should take in order to avoid adverse effects, or Determine potential weaknesses of harmful pathogens as a precursor for drug development. To this end, the University of Tuebingen and the University of California, San Diego, established a joint project with the aim of developing new computational methods that make it possible to model all levels of biological systems. As the result, a wide range of software and database solutions have been created that make building and analyzing systems biology models much more straightforward. For more information, or to download and try systems biology software, visit http://systems-biology.info.
Views: 9016 systems biology
Economics and Probabilistic Machine Learning
 
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David Blei of Columbia University opens the Becker Friedman Institute’s conference on machine learning in economics with an overview of how probabilistic machine learning techniques can be applied in economics.
What is INFORMATION THEORY? What does INFORMATION THEORY mean? INFORMATION THEORY meaning
 
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What is INFORMATION THEORY? What does INFORMATION THEORY mean? INFORMATION THEORY meaning. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Information theory studies the quantification, storage, and communication of information. It was originally proposed by Claude E. Shannon in 1948 to find fundamental limits on signal processing and communication operations such as data compression, in a landmark paper entitled "A Mathematical Theory of Communication". Now this theory has found applications in many other areas, including statistical inference, natural language processing, cryptography, neurobiology, the evolution and function of molecular codes, model selection in ecology, thermal physics, quantum computing, linguistics, plagiarism detection, pattern recognition, and anomaly detection. A key measure in information theory is "entropy". Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process. For example, identifying the outcome of a fair coin flip (with two equally likely outcomes) provides less information (lower entropy) than specifying the outcome from a roll of a die (with six equally likely outcomes). Some other important measures in information theory are mutual information, channel capacity, error exponents, and relative entropy. Applications of fundamental topics of information theory include lossless data compression (e.g. ZIP files), lossy data compression (e.g. MP3s and JPEGs), and channel coding (e.g. for Digital Subscriber Line (DSL)). The field is at the intersection of mathematics, statistics, computer science, physics, neurobiology, and electrical engineering. Its impact has been crucial to the success of the Voyager missions to deep space, the invention of the compact disc, the feasibility of mobile phones, the development of the Internet, the study of linguistics and of human perception, the understanding of black holes, and numerous other fields. Important sub-fields of information theory include source coding, channel coding, algorithmic complexity theory, algorithmic information theory, information-theoretic security, and measures of information. Information theory studies the transmission, processing, utilization, and extraction of information. Abstractly, information can be thought of as the resolution of uncertainty. In the case of communication of information over a noisy channel, this abstract concept was made concrete in 1948 by Claude Shannon in his paper "A Mathematical Theory of Communication", in which "information" is thought of as a set of possible messages, where the goal is to send these messages over a noisy channel, and then to have the receiver reconstruct the message with low probability of error, in spite of the channel noise. Shannon's main result, the noisy-channel coding theorem showed that, in the limit of many channel uses, the rate of information that is asymptotically achievable is equal to the channel capacity, a quantity dependent merely on the statistics of the channel over which the messages are sent. Information theory is closely associated with a collection of pure and applied disciplines that have been investigated and reduced to engineering practice under a variety of rubrics throughout the world over the past half century or more: adaptive systems, anticipatory systems, artificial intelligence, complex systems, complexity science, cybernetics, informatics, machine learning, along with systems sciences of many descriptions. Information theory is a broad and deep mathematical theory, with equally broad and deep applications, amongst which is the vital field of coding theory. Coding theory is concerned with finding explicit methods, called codes, for increasing the efficiency and reducing the error rate of data communication over noisy channels to near the Channel capacity. These codes can be roughly subdivided into data compression (source coding) and error-correction (channel coding) techniques. In the latter case, it took many years to find the methods Shannon's work proved were possible. A third class of information theory codes are cryptographic algorithms (both codes and ciphers). Concepts, methods and results from coding theory and information theory are widely used in cryptography and cryptanalysis. See the article ban (unit) for a historical application. Information theory is also used in information retrieval, intelligence gathering, gambling, statistics, and even in musical composition.
Views: 2772 The Audiopedia
What Is SPATIAL ECONOMETRICS? SPATIAL ECONOMETRICS Definition & Meaning
 
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What is SPATIAL ECONOMETRICS, What does SPATIAL ECONOMETRICS mean, SPATIAL ECONOMETRICS meaning, SPATIAL ECONOMETRICS definition, SPATIAL ECONOMETRICS explanation Spatial econometrics is the field where spatial analysis and econometrics intersect. The term “spatial econometrics” was introduced for the first time by the Belgian economist Jean Paelinck (universally recognised as the father of the discipline) in the general address he delivered to the annual meeting of the Dutch Statistical Association in May 1974 (Paelinck and Klaassen, 1979). In general, econometrics differs from other branches of statistics in focusing on theoretical models, whose parameters are estimated using regression analysis. Spatial econometrics is a refinement of this, where either the theoretical model involves interactions between different entities, or the data observations are not truly independent. Thus, models incorporating spatial auto-correlation or neighborhood effects can be estimated using spatial econometric methods. Such models are common in regional science, real estate economics, education economics, housing market and many others. Adopting a more general view, in the by-law of the Spatial Econometrics Association, the discipline is defined as the set of “models and theoretical instruments of spatial statistics and spatial data analysis to analyse various economic effects such as externalities, interactions, spatial concentration and many others” (Spatial Econometrics Association, 2006). Recent developments tend to include also methods and models from social network econometrics. Source: Wikipedia.org
Views: 4 Audiopedia
Sherry Farrell Racette - "Escaping the Cage: Cultural Performance as Activism, 1890-1951"
 
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Sherry Farrell Racette presents the paper "Escaping the Cage: Cultural Performance as Activism, 1890-1951." Part of Imagining History: A Canadian Women Artists History Initiative Conference, May 3-5 2012, Concordia University. This video has been created for educational purposes only. If you are the copyright holder to any of the images projected in the video and you object to their use in this fashion, please contact [email protected] .
Views: 399 CWAHI Concordia
Data Science De-Mystified with Dun and Bradstreet (CXOTalk #274)
 
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What is data science and how can we use it in business most effectively? Data science is not about the latest shiny tools, but understanding business problems and data. Industry analyst Michael Krigsman, the host of CXOTalk, speaks with one of the world's foremost data scientists to explore this exciting frontier. For more information and the podcast: https://www.cxotalk.com/episode/demystifying-data-science Anthony Scriffignano is the Chief Data Scientist at Dun and Bradstreet. He has over 35 years experience in information technologies, Big-4 management consulting, and international business. Sciffignano leverages deep data expertise and global relationships to position Dun & Bradstreet with strategic customers, partners, and governments.
Views: 7852 CXOTALK
Towards extreme-environment robotics
 
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At Keio University, the Ishigami Laboratory, in the Faculty of Science and Technology, Department of Mechanical Engineering, is investigating robotic mobility systems. The main mission of this group is to perform fundamental and applied research for application to extreme environments, notably lunar and planetary rovers. Q "In our lab, we focus on field robotics that works for extreme environments. For example, we investigate theinteraction mechanics between robots and sandy surfaces, taking into account "off-the-road locomotion."Also, because such robots would be deployed in unknown environments, we also work on vision systems such as cameras and laser rangefinders." In this research, there are three key concepts: vehicle-terrain interaction mechanics, autonomous mobility systems, and robotic device development. In vehicle-terrain interaction mechanics, the researchers analyze vehicle behavior using a dynamic simulator. They're also developing vehicle-slip compensation systems and in-wheel-sensor systems. Q "In the study of interaction mechanics, we first focus on a wheel itself using a "single-wheel testbed." We put just one wheel on the testbed, and perform experimental runs to obtain wheel force data under different sets of slip parameters. Meanwhile, we numerically calculate wheel force based on a wheel-sand interaction model we developed. Then, we compare the experimental results with the numerical ones, so we can evaluate how valid the interaction model is. Applying this approach to a whole robot-vehicle system, it is possible to simulate how the robot behaves dynamically in an unknown environment. That's the key approach in this research." Q "Sand flow investigation has received especially close attention in recent years. In our lab, of course, we've recently taken such an approach, called particle image velocimetry, or PIV, which has been widely used in fluid mechanics. PIV enables us to clearly determine the sand flow, helping to develop a well-defined interaction model." In the area of autonomous mobility systems, the Ishigami Lab is working on environment recognition using laser rangefinders and camera images, as well as robotlocalization, path planning, teleoperation, and integrated sensory processing systems. Q "For example, in an unknown environment, there aren't any road signs, saying 'there's an obstacle here,' or 'turn right at the next intersection.' Such obstacles should be detected by onboard cameras, or laser rangefinders which operate based on the time-of-flight principle (measuring the time from a laser emission to detection of the reflected laser.). In our research, we effectively utilize such devices to obtain 3D distance data or 3D environment information. Based on these data, the robot itself decides how to travel. Such systems are called autonomous mobility systems." Q "One typical point of our lab is, I would say, we're focusing on mechanics as well as autonomous mobility, applying both hardware and software approaches.. In general, one lab has one specific point of interest for research, and looks more deeply into that, but in our lab, we work on mechanics and also on autonomous mobility systems, so we pursue several topics in parallel. Robots consist of integrated technology, so we consider them as total systems. In addition, another feature of our research is, we consider that field tests are extremely important. We actually take our robots to outdoor environments such as volcanic regions on Izu Oshima and Mt. Aso, and operate them in rough terrain, to test how they act in actual environments." Q "The field of robotics comprises a variety of technologies. So, rather than sticking to a single academic discipline, we'd like students to do research from a broad perspective."
Epidemiologist and Global Health Advocate Gregg Gonsalves | 2018 MacArthur Fellow
 
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Gregg Gonsalves is an epidemiologist and global health advocate working at the intersection of human rights and public health research and practice to address inequities in global health. The MacArthur Fellowship is a $625,000, no-strings-attached grant for individuals who have shown exceptional creativity in their work and the promise to do more. Learn more at www.macfound.org/macfellow and explore their stories on social media with the hashtag #MacFellow.
Views: 1384 macfound
The Future of Computational Journalism
 
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Data science and algorithms are reshaping how the news is discovered and reported At a recent event bringing together voices from the School of Engineering and the School of Humanities and Sciences, two Stanford professors engaged in a moderated discussion about the evolving field of computational journalism. Jay Hamilton, the Hearst Professor of Communication and director of the Journalism Program, and Maneesh Agrawala, professor of computer science and director of the Brown Institute for Media Innovation, shared their complementary perspectives on the many questions facing journalism today and where they might lead tomorrow. The conversation centered on how converging social currents and disruptive technologies have roiled newsrooms on the local, national and international levels. Computational journalism, Hamilton said, can refer to the set of tools that journalists use to discover, tell or distribute stories. But it’s also “reporting by, through and about algorithms.” The Associated Press, for example, writes about 4,000 stories by algorithm each time companies’ quarterly earnings reports come out — a massive increase from the 300 or so companies that can be covered by human reporters. In addition to such computer-assisted reporting, Agrawala spoke about how computers can be used to synthesize audio and video stories and create visualizations that provide critical context for data. The two professors also spoke about the great need for journalists to find ways to hold algorithms — like the ones that curate our newsfeeds or influence public policies — accountable. “One of the questions that we face as a society is understanding some of the algorithms that are delivering information to us,” Agrawala said. Hamilton agreed, adding that the biggest problem he sees facing journalism right now are the stories that get missed due to the collapse of the business models of local newspapers. “If you look across the country, there are city councils that don’t have a reporter covering them, there are school boards voting and making decisions and nobody is watching. So I think that’s something where computational journalism can really make an impact,” he said. “If you have a strong interest in engineering and data, try to help us figure out the stories that go untold, especially at the local level.”
Tech Driven Education Reform A Model for Simultaneously Improving Retention and Performance in STEM
 
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Presented by Dr. Rob Petros, Assistant Professor, University of North Texas A major overhaul of our higher education system is desperately needed to increase interest and competence in STEM disciplines. Currently only 40% of entering college students that declare STEM majors complete degrees in STEM disciplines. The efflux of students from STEM majors has been attributed in part to the teaching style that has been used in most lower-level science courses, which is a traditional slideshow and lecture format. This style of teaching has persisted even in the presence of convincing discipline-based education research data that other strategies can increase student attainment of learning outcomes. Engaged, learner-focused activities transparently linked to student learning outcomes have been especially effective for improving student learning in large enrollment classes; however, implementation can be difficult because of the significant time needed to conduct such activities while still covering all the required material and because of the difficulty in demonstrating student attainment of learning outcomes. One way to create the time needed to include engaged learning activities in classroom activities is to make use of recent innovations in technology to flip the classroom. The UNT NextGen course redesign is an outcome-based model that is predicated on the seamless alignment of course objectives with instructional strategies and assessment, which allows for student attainment of course goals to be explicitly quantified. Outcome based course redesign is providing valid quantitative and qualitative measures of student understanding, content mastery and synthesis, the model and best practices for which can be applied to a wide variety of courses to foster institutional change. The model also facilitates identification (and redesign) of areas where student attainment is low. This presentation will use the NextGen redesign of a large enrollment (~200 students) organic course as an example of best practices to improve student performance, retention, and interest in all STEM disciplines. Retention rates as high as 90% have been achieved without sacrificing student performance. (Recorded on Tuesday, January 27th, 2015)
Views: 70 ExamSoft
What Is A Computer Engineer?
 
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Computer engineering? Live sciencecomputer engineering degrees & careers. Uh computer (systems) engineering [email protected] arizona state. Every automated device has a software or they use math and science to solve problems create new products services. Electrical computer engineering, general college degree programs the engineering program queen's ece universitycomputer 2017 2018 catalog. Wikipedia wiki computer_engineering url? Q webcache. Read for complete career guidance and lead others in other words, computer engineers build computers such as pcs, workstations, supercomputers. Googleusercontent search. Hardware engineers focus their skills on computer systems and components, designing microprocessors, circuit boards, routers other embedded devices embed computers in machines systems, build networks to transfer data, make computers, faster, smaller, more capable 17 oct 2014 engineering is the branch of that integrates electronic with sciences information for students about training united states 20 2016. What is ece? Electrical & computer engineering. Computer engineers career info & job description study computer engineering what's the difference what is all about? Computerscienceonline. A computer engineer, also called a software is responsible for developing, testing and evaluating the that make our computers work 28 oct 2013 question i have gotten lot lately has to do with differences similarities between science engineering discipline which resides at intersection of electrical. Learn about different ce industries and what it takes to be a successful computer engineer career option as is one of the best opportunities up for grabs right now. Computer engineers are often explore in demand and emerging careers computer engineering. They also build computer based systems such as those (systems) engineering enables students to engage in the design of integrated hardware and software solutions for technical problems. Learn how to find schools and universities with strong programs for this did you know recent surveys have shown that electrical computer engineers are amongst the highest demand university graduates rmit offers a range of newly accredited bachelor degrees, master degrees associate in network engineering, electronic major engineering degree awarded science (bsce) calendar type quarter total credit hours 192. They are in computer engineering you'll learn how to develop, design, and test software, networks, protocols. Computer engineer career options guidance for computer engineering frequently asked questions. As an electrical and computer engineer you will work at the heart of explore engineering, general studies whether it's right major for. How to become a what is computer engineering? What Live science. Computer engineers who are they and what do do? . What is computer engineering? Youtube. Computer engineering is a discipline that integrates several fields of electrical and computer science required to develop hardware software at the career level, there are two main avenues. Computer engineering? Live science computer engineering wikipedia en.
Views: 7 Question Text
Peter Bailis: MacroBase, Prioritizing Attention in Fast Data Streams | Talks at Google
 
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Professor Peter Bailis of Stanford provides an overview of his current research project, Macrobase, an analytics engine that provides efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. This is part of Google Cloud Advanced Technology Talks, a series dedicated to bringing cutting edge research and prestigious researchers to speak at Google Cloud. All speakers are leading experts and innovators within their given fields of research. Peter Bailis is an assistant professor from Stanford University.
Views: 1969 Talks at Google
Bioconductor Workshop 1: R/Bioconductor Workshop for Genomic Data Analysis
 
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The Computational Biology Core (CBC) at Brown University (supported by the COBRE Center for Computational Biology of Human Disease) and R/Bioconductor Staff team up to provide training on analysis, annotation, and visualization of Next Generation Sequencing (NGS) data. For more info: https://www.brown.edu/academics/computational-molecular-biology/bioconductor-workshop-1-rbioconductor-workshop-genomic-data-analysis Wednesday, February 7th 2018 Brown University
Views: 436 Brown University
Loud Luxury feat. brando - Body (Official Lyric Video)
 
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Check out the music video of the new single of Loud Luxury - Love No More: https://www.youtube.com/watch?v=PJF0SBwfDq8 Listen or download "Loud Luxury feat. brando - Body": https://ARMAS1328.lnk.to/BodyYA Summer-tinged and mesmeric from the get-go, ‘Body’ puts shame to the catchiest songs of the season. From the well-timed vocals of brando to the upbeat chords and meticulous arrangement, it makes for a record that never falters. Heeding the cries of music lovers for quality music, this brilliant production from Loud Luxury is on a level of its own. Stream more Armada Music hits here: https://WeArmada.lnk.to/PLYA Subscribe to Armada TV: http://bit.ly/SubscribeArmada #LoudLuxury #Body #LoudLuxuryBody Connect with Armada Music ▶https://www.facebook.com/armadamusic ▶https://twitter.com/Armada ▶https://soundcloud.com/armadamusic
Views: 78814961 Armada Music
PRACTICE: Outside In | Inside Out
 
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This symposium considers discourse on contemporary issues of design practice in two parts: the external pressures of economic, environmental, and political systems, and internal forces of tools, techniques, and strategies for design. Addressing the multifaceted nature of the profession, we will explore themes for the design of practice, such as work and labor, tools and technology, and ethics and agency. The symposium highlights potential avenues for the growth and constitution of practice, as well as the issues currently at stake within the profession. The following discussions confront pressing questions regarding the shifting responsibilities of design practice, and the future of practice itself. This symposium is generously sponsored by the Carl M. Sapers Ethics in Practice Fund, and co-hosted by the GSD Practice Platform and the Department of Architecture. Panelists: Aaron Cayer, Neena Verma, Jesse Keenan, Alison Brooks, Eduard Sancho Pou, Sawako Kaijima, Randy Deutsch, Robert Pietrusko Moderators: Mark Lee, Grace La
Views: 1510 Harvard GSD
HLS Library Book Talk: "Big Data, Health Law, and Bioethics"
 
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On Wednesday Sept. 12, the Harvard Law School Library hosted a book talk and discussion in celebration of the recent publication of "Big Data, Health Law, and Bioethics," edited by I. Glenn Cohen, Holly Fernandez Lynch, Urs Gasser, and Effy Vayena. The talk was co-sponsored by the Petrie-Flom Center for Health Law Policy, Biotechnology and Bioethics and by the Berkman Klein Center for Internet & Society at Harvard University.
Views: 586 Harvard Law School
ОТ АТЕИСТА К СВЯТОСТИ
 
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Ответы на главные вопросы атеистов, верующих, священников, людей, стремящихся обрести путь истинного бессмертия. Черное и белое. Что такое истинное бессмертие и как его достичь при жизни без посредников? Особенности работы сознания, его уловки и фильтры на духовном пути. Штампы сознания в инкубаторе системы. Как стать свободным от оков системы и обрести духовную Любовь и настоящее счастье. В передаче демонстрируется фильм «АТЛАНТИДА. ЭЛИТА В ПОИСКАХ БЕССМЕРТИЯ». ПРАВДА о происхождении элиты в современном человеческом обществе, их поиск бессмертия. Элита – слуги Эля. История допотопной высокоразвитой цивилизации – Атлантиды, упомянутая в мировом литературном наследии Шумера, Вавилонии, Эллады, а также в мифах разных народов мира. Высокоразвитые технологии, борьба за власть, климатическое оружие, ядерная война древности, мегалиты, уникальные технологии пролонгирования жизни за видовой предел, бессмертие в теле для избранных. Факты и доказательства. Как идеология потомков атлантов отразилась на современном мировоззрении человечества? История развития заговора мировой элиты. РЕЗКОЕ И БЫСТРОЕ ИЗМЕНЕНИЕ КЛИМАТА. Последняя черта. Участники передачи: Игорь Михайлович Данилов, Жанна, Татьяна. СОЗНАНИЕ И ЛИЧНОСТЬ https://allatra.tv/video/soznanie-i-lichnost О ДУХОВНОЙ БЛАГОДАТИ https://allatra.tv/video/o-duhovnoj-blagodati Официальный сайт АЛЛАТРА ТВ https://allatra.tv/ Почта: [email protected]
Views: 964701 АллатРа ТВ
economics: National income(gdp,ndp,gnp,nnp,etc) in hindi
 
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basis of economics ... national incone ,GDP,GNP,etc poverty measurement What is economics? Why are some countries rich and some countries poor? Why do women earn less than men? How can data help us understand the world? Why do we ignore information that could help us make better decisions? What causes recessions? Economics can help us answer these questions. Below, we’ve provided links to short articles that illustrate what economics is and how it connects to our everyday lives. Economics can be defined in a few different ways. It’s the study of scarcity, the study of how people use resources and respond to incentives, or the study of decision-making. It often involves topics like wealth and finance, but it’s not all about money. Economics is a broad discipline that helps us understand historical trends, interpret today’s headlines, and make predictions about the coming years. Economics ranges from the very small to the very large. The study of individual decisions is called microeconomics. The study of the economy as a whole is called macroeconomics. A microeconomist might focus on families’ medical debt, whereas a macroeconomist might focus on sovereign debt. What do economists do? Economists have all kinds of jobs, such as professors, government advisors, consultants, and private sector employees. Using theoretical models or empirical data, they evaluate programs, study human behavior, and explain social phenomena. And, their contributions inform everything from public policy to household decisions. Economics intersects many disciplines. Its applications include health, gender, the environment, education, and immigration. You can check out the field’s classification system (called JEL codes) for more topics that economists study. Why should I care about economics? Economics affects everyone’s lives. Learning about economic concepts can help you to understand the news, make financial decisions, shape public policy, and see the world in a new way. If you are a student, you might be wondering about how much economists earn or how to apply to graduate school in economics. We have resources on everything from learning more about economics to preparing for a career in economics. If you are a journalist, you might want research summaries and complimentary access to our journal publications — both reliable sources of current economic information. If you are an educator, you might be looking for ways to make economics more exciting in the classroom, get complimentary journal access for high school students, or incorporate real-world examples of economics concepts into lesson plans. Or, you might just want to learn more; our Research Highlight series is a great place to start. Economists can study a wide variety of topics. The following videos highlight some of the ways economists use data to explore everything. -~-~~-~~~-~~-~- Please watch: "Gate -thermodynamics 1-7(system,boundary ,surrounding,process,cycle,graph drawing)" https://www.youtube.com/watch?v=OySQw4Qim9Y -~-~~-~~~-~~-~-
Views: 381190 ajaxxman
Just Mercy: Race and the Criminal Justice System with Bryan Stevenson
 
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Bryan Stevenson, acclaimed public interest lawyer and founder and executive director of the Equal Justice Initiative delivers the 2016 Anne and Loren Kieve Distinguished Speaker Lecture on race and the criminal justice system. A roundtable conversation featuring Jennifer Eberhardt, Gary Segura, Robert Weisberg, JD ’79, Bryan Stevenson, and Katie Couric follows Bryan Stevenson's keynote address. OpenXChange is a year-long, student-focused initiative on campus that aims to encourage meaningful dialogue around tough issues. This is the first in a series of discussions with Stanford faculty and global experts on criminal justice, inequality and international conflict. This event was recorded on Wednesday, Jan 13, 2016
Views: 7969 Stanford Alumni
Yelawolf - Till It’s Gone
 
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iTunes: http://smarturl.it/TillItsgone Sign up for updates: http://smarturl.it/Yelawolf.News Music video by Yelawolf performing Till It’s Gone. (C) 2014 Interscope Records Best of Yelawolf: https://goo.gl/vy7NZQ Subscribe here: https://goo.gl/ynkVDL
Views: 84890893 YelawolfVEVO
The Ethics and Governance of AI opening event, February 3, 2018
 
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Chapter 1: 0:04 - Joi Ito Chapter 2: 1:03:27 - Jonathan Zittrain Chapter 3: 2:32:59 - Panel 1: Joi Ito moderates a panel with Pratik Shah, Karthik Dinakar, and Vikash Mansinghka Chapter 4: 3:19:13 - Panel 2: Joi Ito moderades a panel with Kade Crockford (ACLU), Chris Bavitz (), and Adam Foss() discuss the implications of AI for social and criminal justice. More information at: https://www.media.mit.edu/courses/the-ethics-and-governance-of-artificial-intelligence/ License: CC-BY-4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
Views: 4569 MIT Media Lab
Machine Learning Course, Training, Institute in Mohali & Chandigarh | ITRONIX SOLUTIONS
 
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Machine Learning Course, Training, Institute in Mohali & Chandigarh | ITRONIX SOLUTIONS Itronix Solutions is one of the best training institute in Mohali and Chandigarh for machine Learning. The course offered by Itronix Solutions covers exactly how to acquire practical hands on Skills in the easiest, fastest and cheapest way possible.Students will be trained under highly qualified experts and industry practitioners Our Machine Learning Training in Mohali aims to teach the complete Data Warehousing Concepts in an easier way. Machine Learning using Python programming We are the Best Machine Learning Training Institute in Mohali in-terms of a syllabus and expert teaching. We are covering almost all the transformations which are required for companies. Machine learning is a sub field of Artificial intelligence and intersects with cognitive techniques, Learning theory and contingency theory among others. It could be defined as the ability of a machine to improve its own performance through the use of a software that employs artificial intelligence techniques to mimic the ways by which humans seem to learn such as repetition and experience. Machine learning can still be defined as studying the theory automatically from the data through a process of inference model fitting or learning from examples that are ideally suited for areas with lots of data in the absence of a general theory It is a scientific discipline that is concerned with the design and development of algorithms that allow computers to emerge behaviours based on observed data such as from sensor data or databases. Python is a emerging language to develop machine learning applications. As a dynamic language it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for python. Python has most powerful open source libraries for deep learning, data wrangling and data visualization to learn effective strategies and best practices to improve and optimize machine learning systems and algorithms and it is the field of study interested in the advancement of computer algorithms to revolutionize data into smart action. Growth in data required additional reckon power which in turn spurred the development of statistical methods for analysing large datasets. This field originated in an environment where the available data statistical methods promptly and concurrently evolved this created a revolution of advancement allowing even better and more interesting data. Machine learning at its base is concerned with converting data into actionable work This reality makes machine learning well appropriate to the present day era of big data and given the growing prominence of python a cross platform zero cost statistical programming environment to apply in machine learning, whether you are new to data science or a veteran machine learning with python offers a powerful set of methods for quick insight of your data. Machine learning methods will help you gain hands on experience in the real world issues that will transform your thinking about data .Machine learning with python will provide you with the analytical tools required to quickly gain insight from complex data. Website : http://machinelearning.org.in/ https://www.itronixsolutions.com/machine-learning-training-mohali/ http://www.itronixsolution.com/machine-learning-training-mohali/
Views: 64740 Itronix Solution
Carmine Gallo: Three Secrets All Inspiring Messages Share
 
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Carmine Gallo shares the three simple secrets all inspiring messages share, and how inspiring executives and entrepreneurs tell their brand or product story in a way that's understandable, memorable and emotional. Gallo addressed the Stanford Graduate School of Business as part of the Mastery in Communication Initiative's Expert Speaker Series. Gallo is a best-selling author, communications coach, and keynote speaker. He is a former reporter and anchor for CNN and CBS. He has sat down with many of the most dynamic and respected business leaders of our time. Gallo Communications website: http://www.gallocommunications.com Stanford GSB Mastery in Communication Initiative: http://www.gsb.stanford.edu/mastery See related video by Carmine Gallo at the Stanford Graduate School of Business Sell Your Ideas the Steve Jobs Way http://www.youtube.com/watch?v=0q-wvAIeUgk
Patrick Ball ─ Digital Echoes: Understanding Patterns of Mass Violence with Data and Statistics
 
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Patrick Ball is the director of research at Human Rights Data Analysis Group. Data about mass violence can seem to offer insights into patterns: is violence getting better, or worse, over time? Is violence directed more against men or women? But in human rights data collection, we (usually) don’t know what we don’t know --- and worse, what we don’t know is likely to be systematically different from what we do know. This talk will explore the assumption that nearly every project using data must make: that the data are representative of reality in the world. We will explore how, contrary to the standard assumption, how statistical patterns in raw data tend to be quite different than patterns in the world. Statistical patterns in data reflect how the data was collected rather than changes in the real-world phenomena data purport to represent. Using analysis of killings in Iraq, homicides committed by police in the US, killings in the conflict in Syria, and homicides in Colombia, we will contrast patterns in raw data with estimates total patterns of violence—where the estimates correct for heterogeneous underreporting. The talk will show how biases in raw data can be addressed through estimation, and explain why it matters.
Past, Present and Future of AI / Machine Learning (Google I/O '17)
 
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We are in the middle of a major shift in computing that's transitioning us from a mobile-first world into one that's AI-first. AI will touch every industry and transform the products and services we use daily. Breakthroughs in machine learning have enabled dramatic improvements in the quality of Google Translate, made your photos easier to organize with Google Photos, and enabled improvements in Search, Maps, YouTube, and more. We’re also sharing the underlying technology with developers and researchers via open-source software such as TensorFlow, academic publications, and a full suite of Cloud machine learning services. Join this session to hear some of Alphabet's top machine learning experts discuss their cutting-edge research and the opportunities they see ahead. See all the talks from Google I/O '17 here: https://goo.gl/D0D4VE Watch more Android talks at I/O '17 here: https://goo.gl/c0LWYl Watch more Chrome talks at I/O '17 here: https://goo.gl/Q1bFGY Watch more Firebase talks at I/O '17 here: https://goo.gl/pmO4Dr Subscribe to the Google Developers channel: http://goo.gl/mQyv5L #io17 #GoogleIO #GoogleIO2017
Views: 110765 Google Developers
Research in Focus: Deep Learning Research and the Future of AI
 
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AI deep learning expert and University of Montreal Professor Yoshua Bengio talks about deep learning—what it is, how it got there, where it’s going, and how you can learn more about it. He discusses the latest in neural nets, unsupervised learning, generative adversarial networks, soft attention, optimization, and more. See more on this video at https://www.microsoft.com/en-us/research/event/faculty-summit-2017/
Views: 18378 Microsoft Research
SOCIOLOGY - Max Weber
 
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Max Weber explained that modern capitalism was born not because of new technology or new financial instruments. What started it all off was religion. SUBSCRIBE to our channel for new films every week: http://tinyurl.com/o28mut7 If you like our films take a look at our shop (we ship worldwide): http://www.theschooloflife.com/shop/all/ Brought to you by http://www.theschooloflife.com Produced by Stuart Odunsi for Mad Adam Films: http://www.madadamfilms.co.uk #TheSchoolOfLife
Views: 1152040 The School of Life
Political science
 
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Political science is a social science discipline concerned with the study of the state, nation, government, and politics and policies of government. Aristotle defined it as the study of the state. It deals extensively with the theory and practice of politics, and the analysis of political systems, political behavior, and political culture. Political scientists "see themselves engaged in revealing the relationships underlying political events and conditions, and from these revelations they attempt to construct general principles about the way the world of politics works." Political science intersects with other fields; including economics, law, sociology, history, anthropology, public administration, public policy, national politics, international relations, comparative politics, psychology, political organization, and political theory. Although it was codified in the 19th century, when all the social sciences were established, political science has ancient roots; indeed, it originated almost 2,500 years ago with the works of Plato and Aristotle. Political science is commonly divided into distinct sub-disciplines which together constitute the field: This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 36329 Audiopedia
Nobel Laureate Joseph Stiglitz on Globalization, Inequality and Capitalism
 
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Joseph Stiglitz, winner of the 2001 Nobel Prize in economics will offer his insights on “Globalization, Inequality and Capitalism” as the Cornelson Distinguished Lecturer. Among the most influential economists in modern times, Stiglitz has served as the chair of the Council of Economic Advisers to President Bill Clinton, and as senior vice president and chief economist of the World Bank. He was named among Time magazine’s 100 most influential people in the world and is the author of books including The Price of Inequality and Globalization and Its Discontents. The lecture will be broadcast live at 4:30 p.m., Friday, March 16.
Views: 7596 Davidson College
BIO Distinguished Lecture Series – Michael Dietze
 
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BIO Distinguished Lecture Series – Michael Dietze: "Solving the challenge of predicting nature: How close are we and how do we get there?" Is nature predictable? If so, how can we better manage and conserve ecosystems? Near-term ecological forecasting is an emerging interdisciplinary research area that aims to improve researchers' ability to predict ecological processes on timescales that can be validated and updated. On September 27, 2018, the National Science Foundation's (NSF) Directorate for Biological Sciences invited media and members of the public to a Distinguished Lecture by Dr. Michael Dietze of Boston University. An ecologist who leads the university's Ecological Forecasting Laboratory, Dr. Dietze discussed the challenges and opportunities in near-term ecological forecasting, which span advances in environmental monitoring, statistics and cyberinfrastructure. Dr. Dietze is interested in ways that iterative forecasts can improve and accelerate basic environmental science while making that science more directly relevant to society. Dietze's plant ecology lab led the development of PEcAn, the Predictive Ecosystems Analyzer, an open-source software platform for analyzing diverse data and ecosystem models, funded by grants from NSF, NASA and others. In his Distinguished Lecture, Dr. Dietze presents a framework to understand the predictability of a wide range of ecological processes and highlighted ongoing efforts to build an ecological forecasting community of practice.
Machine Learning Bias and Fairness with Timnit Gebru and Margaret Mitchell: GCPPodcast 114
 
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Original post: https://www.gcppodcast.com/post/episode-114-machine-learning-bias-and-fairness-with-timnit-gebru-and-margaret-mitchell/ This week, we dive into machine learning bias and fairness from a social and technical perspective with machine learning research scientists Timnit Gebru from Microsoft and Margaret Mitchell (aka Meg, aka M.) from Google. They share with Melanie and Mark about ongoing efforts and resources to address bias and fairness including diversifying datasets, applying algorithmic techniques and expanding research team expertise and perspectives. There is not a simple solution to the challenge, and they give insights on what work in the broader community is in progress and where it is going.
Views: 1462 Google Cloud Platform
The Live Wire - Knowledge Discovery in Databases
 
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Dr. Pamela Thompson, Adjunct faculty member, and Lavanya Loganarayanan, recent graduate, were the guests on the August 20 edition of “The Live Wire,” Inside UNC Charlotte’s streaming webcast. They discussed the course “Knowledge Discovery in Databases”, which is part of UNC Charlotte’s Data Science Initiative, and how UNC Charlotte students have analyzed diverse data sets related to sharks and have discovered that certain patterns emerge.
Critical Race Theory and Education
 
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Gloria Ladson-Billings is the author of several books, chapters and articles, including Crossing Over to Canaan: The Journey of New Teachers in Diverse Classrooms. A former editor of the American Education Research Journal, Ladson-Billings was elected in 2005 to the National Academy of Education. She is Kellner Family Chair in Urban Education and Professor of Curriculum and Instruction and Educational Policy Studies at the University of Wisconsin-Madison. Her talk on culturally relevant teaching is intended for a general audience.
Space Tug by Murray Leinster, read by Mark Nelson, complete unabridged audiobook
 
06:12:58
Unabridged audio book - Genre(s): Action & Adventure Fiction, Science Fiction Space Tug by Murray Leinster (1896 - 1975) Joe Kenmore heard the airlock close with a sickening wheeze and then a clank. In desperation he turned toward Haney. "My God, we've been locked out!" Through the transparent domes of their space helmets, Joe could see a look of horror and disbelief pass across Haney's face. But it was true! Joe and his crew were locked out of the Space Platform. Four thousand miles below circled the Earth. Under Joe's feet rested the solid steel hull of his home in outer space. But without tools there was no hope of getting back inside. Joe looked at his oxygen meter. It registered thirty minutes to live. (Summary from Gutenberg text) Read by: Mark Nelson Book Coordinator: Mark Nelson Meta Coordinator: Mary Anderson Proof Listener: Buechermaus 00:00:00 Chapter 01 00:49:25 Chapter 02 01:24:35 Chapter 03 02:10:35 Chapter 04 02:47:45 Chapter 05 03:03:35 Chapter 06 03:42:27 Chapter 07 04:00:43 Chapter 08 04:41:31 Chapter 09 05:19:42 Chapter 10 05:48:28 Chapter 11 Running Time: 06:12:56 Murray Leinster playlist -» http://www.youtube.com/playlist?list=PLLG03REJaYO-mSxPZUv5X4NfCqJJziM0U Audio Recording © courtesy of Librivox This video: © Copyright 2013. PublicAudioLibrary. All Rights Reserved.
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