Home
Search results “Statistics and data mining intersecting disciplines definition”
QSS: the Intersection of Data Science and Liberal Arts @ Emory
 
01:47
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: 479 Emory University
RSS President's address 2015 - Statistics: a Data Science for the 21st Century
 
55:32
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: 2229 RoyalStatSoc
The Discipline of Organizing: The Intellectual Intersection of the Information Schools
 
01:10:58
More info: http://www.ischool.berkeley.edu/newsandevents/events/deanslectures/20121003glushko Slides: http://people.ischool.berkeley.edu/~glushko/glushko_files/TDOBerkeleyOct2012.pdf School of Information Dean's Lecture Wednesday, October 3, 2012 The Discipline of Organizing: The Intellectual Intersection of the Information Schools Speaker: Robert J. Glushko The Information School community suffers from a splintered identity, because the schools differ greatly in the problem domains emphasized, the degrees offered, the courses required, and the types of jobs found by graduates. But despite the obvious differences among them, we believe there is an intellectual intersection among the I Schools in the study of "Organizing Systems" — intentionally arranged collections of resources and the interactions they support. All organizing systems share common activities: identifying resources to be organized; organizing resources by describing and classifying them; designing resource-based interactions; and maintaining resources and organization over time. This framework exposes design concepts and patterns that apply to libraries, museums, business information systems, personal information management, and social computing contexts. In this talk I will present the key ideas of the Organizing System perspective, discuss how it is being collaboratively taught this semester at several I Schools, and describe how its transdisciplinary character has inspired new concepts for customized e-books as its delivery platform. Bio: Bob Glushko is an adjunct professor at the School of Information, where he has been since 2002. Glushko has over thirty years of R&D, consulting, and entrepreneurial experience in information systems and service design, content management, electronic publishing, Internet commerce, and human factors in computing systems. He founded or co-founded four companies, including Veo Systems in 1997, which pioneered the use of XML for electronic business before its 1999 acquisition by Commerce One. Veo's innovations included the Common Business Library (CBL), the first native XML vocabulary for business-to-business transactions, and the Schema for Object-Oriented XML (SOX), the first object-oriented XML schema language. From 1999--2002 he headed Commerce One's XML architecture and technical standards activities and was named an engineering fellow in 2000. In 2008 he co-founded Document Engineering Services, an international consortium of expert consultants in standards for electronic business. From 2005--2010, Glushko was a member of the board of directors for OASIS, an international consortium that drives the development, convergence, and adoption of "open standards for the global information society," and is currently on the board of directors for the Open Data Foundation, dedicated to the adoption of global metadata standards for statistical data. He is the President of the Robert J. Glushko and Pamela Samuelson Foundation, which sponsors the annual Rumelhart Prize in Cognitive Science. In 2011 he was named one of 50 UCSD Alumni Leaders by the UC San Diego Alumni Association to celebrate the university's 50th anniversary.
Professor Mark Girolami: "Probabilistic Numerical Computation: A New Concept?"
 
01:01:07
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 #TuringLectures
Running Agile Data Science Teams | Data Dialogs 2015
 
42:27
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.
Professor Mark Newman: "Epidemics, Erdos numbers, and the Internet"
 
55:28
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 #TuringLectures
Professor Luciano Floridi: "Ethics in the Age of Information"
 
01:14:47
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 #TuringLectures
Professor Helen Margetts: "The Data Science of Politics"
 
01:02:05
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 #TuringLectures
Professor Gareth Roberts: "New challenges in Computational Statistics"
 
01:03:49
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 #TuringLectures
Multiple Regression - Dummy variables and interactions - example in Excel
 
30:31
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: 236645 Jason Delaney
A review of machine learning techniques for anomaly detection - Dr David Green
 
21:46
The Alan Turing Institute, headquartered in the British Library, London, was created as the national institute for data science in 2015. In 2017, as a result of a government recommendation, we added artificial intelligence to our remit. The Institute is named in honour of Alan Turing (23 June 1912 – 7 June 1954), whose pioneering work in theoretical and applied mathematics, engineering and computing are considered to be the key disciplines comprising the fields of data science and artificial intelligence. Five founding universities – Cambridge, Edinburgh, Oxford, UCL and Warwick – and the UK Engineering and Physical Sciences Research Council created The Alan Turing Institute in 2015. Eight new universities – Leeds, Manchester, Newcastle, Queen Mary University of London, Birmingham, Exeter, Bristol, and Southampton – are set to join the Institute in 2018.# #TuringSeminars
Lecture: Mathematics of Big Data and Machine Learning
 
38:16
MIT RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 View the complete course: https://ocw.mit.edu/RESLL-005F12 Instructor: Jeremy Kepner Jeremy Kepner talked about his newly released book, "Mathematics of Big Data," which serves as the motivational material for the D4M course. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 95911 MIT OpenCourseWare
Systems Biology: A Short Overview
 
02:58
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: 10118 systems biology
Lecture 03 -The Linear Model I
 
01:19:44
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: 243254 caltech
Machine Learning Course, Training, Institute in Mohali & Chandigarh | ITRONIX SOLUTIONS
 
06:15
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: 64864 Itronix Solution
What is INFORMATION THEORY? What does INFORMATION THEORY mean? INFORMATION THEORY meaning
 
05:19
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: 3011 The Audiopedia
Correlation in Geography
 
22:02
Correlation between two or more variables is called Correlation. Correlation analysis attempts to determine the degree of relationship between variables. Such correlation is possible in univariate distribution, bi-variate, trivariate series and multivariate series of data correlation may be quantitative, qualitative, or numerical form. Quantitative correlation is expressed in term of direction and magnitude of co- relationship. Correlation may be in positive direction with perfect or partial or zero magnitude while it is also expressed in negative direction with perfect, partial and zero nature o fco- relationship. Qualitative correlation is expressed in linear of curvilinear form.
Views: 738 Dynamic Geography
Data Science in Clinical Research
 
48:11
A PDF of the slides presented can be found here: https://bit.ly/2ILlXx2 Part of the "Biostatistics in Action: Tips for Clinical Researchers" lecture series that is sponsored by the Irving Institute for Clinical and Translational Research - Biostatistics, Epidemiology and Research Design resource, which is supported in part by an NIH Clinical and Translational Science Award (CTSA) through its Center for Advancing Translational Sciences (Grant No, UL1TR001873). The speaker, Jeff Goldsmith, PhD is an Assistant Professor in the Department of Biostatistics at the Mailman School of Public Health. Sponsored by: The Irving Institute for Clinical and Translational Research: http://irvinginstitute.columbia.edu/ In affiliation with: The Department of Biostatistics at the Mailman School of Public Health: https://www.mailman.columbia.edu/become-student/departments/biostatistics
Views: 68 BERD Education
AIG: Data Science in the Insurance Industry and Financial Services (CXOTalk #259)
 
36:21
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: 5266 CXOTALK
Patrick Ball ─ Digital Echoes: Understanding Patterns of Mass Violence with Data and Statistics
 
01:42:31
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.
Micro-Biology: Crash Course History of Science #24
 
12:12
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: 80132 CrashCourse
What Is SPATIAL ECONOMETRICS? SPATIAL ECONOMETRICS Definition & Meaning
 
02:08
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: 16 Audiopedia
Towards extreme-environment robotics
 
04:17
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."
Tech Driven Education Reform A Model for Simultaneously Improving Retention and Performance in STEM
 
42:14
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: 71 ExamSoft
Sherry Farrell Racette - "Escaping the Cage: Cultural Performance as Activism, 1890-1951"
 
22:47
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: 439 CWAHI Concordia
What Is A Computer Engineer?
 
00:47
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
2014 Sol Kanee Lecture - Justice Murray Sinclair
 
02:02:47
The Arthur V. Mauro Centre for Peace and Justice at St. Paul's College, University of Manitoba, is proud to present the Eleventh Annual Sol Kanee Lecture on International Peace and Justice. This year's guest lecturer was Justice Murray Sinclair, Chair of the Truth and Reconciliation Commission of Canada (www.trc.ca). Justice Sinclair addressed the question: What Do We Do About the Legacy of Indian Residential Schools? The lecture took place on Monday, September 29, 2014 at the University of Manitoba. 0:04 Opening Remarks and Welcome: Dr. Sean Byrne, Director, Arthur V. Mauro Centre for Peace and Justice at St. Paul’s College 4:32 Greetings: Dr. Chris Adams, Rector, St. Paul’s College at the University of Manitoba 6:55 Introduction of Justice Murray Sinclair: Dr. Niigaanwewidam Sinclair, Assistant Professor, Native Studies, University of Manitoba 17:20 What Do We Do About the Legacy of Indian Residential Schools? Justice Murray Sinclair Part 1 46:28 Video presentations – Justice Murray included a series of video interviews with residential school survivors as a part of his lecture 53:30 What Do We Do About the Legacy of Indian Residential Schools? Justice Murray Sinclair Part 2 1:29:33 Question and Answer Period: Dr. Sean Byrne, Moderator 1:59:12 Acknowledgement Peace and Conflicts Studies students, Ms. Mary Anne Clarke and Ms. Jennifer Ham acknowledge and thank Justice Sinclair 2:01:18 Concluding Remarks: Dr. Sean Byrne For more information on this and other Mauro Centre events, please visit: www.facebook.com/maurocentre www.umanitoba.ca/colleges/st_pauls/mauro_centre/
Views: 1189 MauroCentre
Economics and Probabilistic Machine Learning
 
01:06:05
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.
Epidemiologist and Global Health Advocate Gregg Gonsalves | 2018 MacArthur Fellow
 
03:19
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: 1600 macfound
What is ECONOMIC GRAPH? What does ECONOMIC GRAPH mean? ECONOMIC GRAPH meaning & explanation
 
03:35
What is ECONOMIC GRAPH? What does ECONOMIC GRAPH mean? ECONOMIC GRAPH meaning - ECONOMIC GRAPH definition - ECONOMIC GRAPH explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. The social science of economics makes extensive use of graphs to better illustrate the economic principles and trends it is attempting to explain. Those graphs have specific qualities that are not often found (or are not often found in such combinations) in other sciences. A common and specific example is the supply-and-demand graph shown at right. This graph shows supply and demand as opposing curves, and the intersection between those curves determines the equilibrium price. An alteration of either supply or demand is shown by displacing the curve to either the left (a decrease in quantity demanded or supplied) or to the right (an increase in quantity demanded or supplied); this shift results in new equilibrium price and quantity. Economic graphs are presented only in the first quadrant of the Cartesian plane when the variables conceptually can only take on non-negative values (such as the quantity of a product that is produced). Even though the axes refer to numerical variables, specific values are often not introduced if a conceptual point is being made that would apply to any numerical examples. More generally, there is usually some mathematical model underlying any given economic graph. For instance, the commonly used supply-and-demand graph has its underpinnings in general price theory—a highly mathematical discipline. n most mathematical contexts, the independent variable is placed on the horizontal axis and the dependent variable on the vertical axis. For example, if f(x) is plotted against x, conventionally x is plotted horizontally and the value of the function is plotted vertically. This placement is often, but not always, reversed in economic graphs. For example, in the supply-demand graph at the top of this page, the independent variable (price) is plotted on the vertical axis, and the dependent variable (quantity supplied or demanded), whose value depends on price, is plotted horizontally. However, when time is the independent variable, and values of some other variable are plotted as a function of time, normally the independent variable time is plotted horizontally, as in the line graph to the right. Yet other graphs may have one curve for which the independent variable is plotted horizontally and another curve for which the independent variable is plotted vertically. For example, in the IS-LM graph shown here, the IS curve shows the amount of the dependent variable spending (Y) as a function of the independent variable the interest rate (i), while the LM curve shows the value of the dependent variable, the interest rate, that equilibrates the money market as a function of the independent variable income (which equals expenditure on an economy-wide basis in equilibrium). Since the two different markets (the goods market and the money market) take as given different independent variables and determine by their functioning different dependent variables, necessarily one curve has its independent variable plotted horizontally and the other vertically.
Views: 395 The Audiopedia
The Future of Computational Journalism
 
01:27:05
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.”
economics: National income(gdp,ndp,gnp,nnp,etc) in hindi
 
58:06
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: 381663 ajaxxman
Bioconductor Workshop 1: R/Bioconductor Workshop for Genomic Data Analysis
 
04:29:57
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: 1060 Brown University
Past, Present and Future of AI / Machine Learning (Google I/O '17)
 
44:33
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: 112579 Google Developers
Exploring Complex, High-Dimensional Data For Hidden Structure - Marvin Weinstein (SETI Talks)
 
01:14:44
SETI Talks archive: http://seti.org/talks Speaker: Marvin Weinstein, Theoretical Physics Group, Stanford University Abstract: How does one search for a needle in a multi-dimensional haystack not knowing what a needle is and not being sure there is one in the haystack? Solving this sort of problem might seem to be impossible, yet this is exactly what Dynamic Quantum Clustering (DQC) manages to do. Several key features of DQC are: it is unbiased in that it makes no assumptions about the type, structure or number of clusters that might exist; it is data agnostic, in that it uses no domain-specific knowledge; it doesn't find clusters when presented with random data; it works when other clustering methods fail to work. These advantages mean that DQC works well for data coming from fields as different from one another as biology, physics, medicine, finance and even national security. Dr. Weinstein's talk will cover examples drawn from many successful applications of DQC. In each of these, conventional clustering methods failed to produce useful results. The examples are real data from a wide variety of disciplines including x-ray absorption spectroscopy, earthquake science, particle physics, condensed matter physics and biology. These vary in size from thousands to millions entries. They convincingly demonstrate DQC's power and flexibility.
Views: 17367 SETI Institute
Loud Luxury feat. brando - Body (Official Lyric Video)
 
03:41
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: 97026734 Armada Music
Data Issues: Multiple Testing, Bias, Confounding, Missing...
 
01:00:59
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
Peter Bailis: MacroBase, Prioritizing Attention in Fast Data Streams | Talks at Google
 
51:35
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: 2090 Talks at Google
The future of artificial intelligence and self-driving cars
 
01:31:25
Stanford professors discuss their innovative research and the new technologies that will transform lives in the 21st century. At a live taping of The Future of Everything, a SiriusXM radio program hosted by Stanford bioengineering professor Russ Altman, two Stanford engineering professors discussed their contributions to two of the tech world’s most cutting edges: artificial intelligence and autonomous vehicles. Computer scientist Fei-Fei Li and mechanical engineer Chris Gerdes spoke about their work pushing the boundaries of what machines can do, and the many ways that our lives will be impacted by interactions with technology in the very near future – if not today. Li outlined some of the major advances that have pushed AI research forward in the years since she entered the field in 2000, a period in which data collection and computing power flourished and “started to converge in a way that most people didn’t expect.” After touching on her seminal work in automated image classification, Li moved on to some of her current projects “using AI to play the guardian angel role in health care.” For instance, she’s working on how sensors installed in senior living facilities can balance care with independence, and track living behaviors such as motion patterns, social activity, nutrition intake and sleep patterns – all of which could help early detection of things like dementia. “This is why I call it a guardian angel. It’s quiet, it’s continuous, it doesn’t interrupt your life, but it’s there for you and providing the help when needed.” As a leader in the field of self-driving cars, Gerdes said he’s confident that we can soon give cars the skills of the very best human drivers, and maybe even better than that. The bigger issues, he said, have more to do with designing public policies for self-driving cars and asking questions like whether we program automated vehicles to do what humans do or what the law says. And we can’t afford to put these questions off. “The proliferation of this technology will be much faster than people realize,” Gerdes said. “The real risk is how do we make sure that it’s accessible, affordable, sustainable transportation for everyone.” Li and Gerdes agreed that the question is less whether artificial intelligence and smart machines will happen, but rather what we need to do to responsibly prepare for them. “With the speed of technology improving, the age of humans and machines coworking and coexisting together has begun,” Li said. “And this is more reason to invest in more basic science research, from technology to laws to moral philosophy and ethics to really give us guidance in terms of how humans can coexist with machines.”
HLS Library Book Talk: "Big Data, Health Law, and Bioethics"
 
01:20:50
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: 762 Harvard Law School
Yelawolf - Till It’s Gone (Official Music Video)
 
04:48
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 #Yelawolf #TillItsGone #Vevo #HipHop #OfficialMusicVideo
Views: 87843612 YelawolfVEVO
Just Mercy: Race and the Criminal Justice System with Bryan Stevenson
 
01:44:09
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: 9410 Stanford Alumni
Lecture 01 - The Learning Problem
 
01:21:28
The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 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 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 849274 caltech
SOCIOLOGY - Max Weber
 
07:23
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: 1200719 The School of Life
Learning To See [Part 1: Introduction]
 
06:21
In this series, we'll explore the complex landscape of machine learning and artificial intelligence through one example from the field of computer vision: using a decision tree to count the number of fingers in an image. It's gonna be crazy. Supporting Code: https://github.com/stephencwelch/LearningToSee welchlabs.com @welchlabs
Views: 137435 Welch Labs
Correlation Is Not Causation - Part 1
 
06:09
Spring 2015 Version
Views: 37627 Soomo Publishing
The Psychology of Love
 
01:27:48
Dr. Frank Conner presents "The Psychology of Love: Do Opposites Attract or Do Birds of a Feather Flock Together?" as part of the 2009-2010 Psychology Lecture Series.
Views: 798876 GRCCtv
Machine Learning Bias and Fairness with Timnit Gebru and Margaret Mitchell: GCPPodcast 114
 
42:47
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: 1514 Google Cloud Platform
Carmine Gallo: Three Secrets All Inspiring Messages Share
 
48:23
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