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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: 396 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
Feature Selection through Lasso
 
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Information technology advances are making data collection possible in most if not all fields of science and engineering and beyond. Statistics as a scientific discipline is challenged and enriched by the new opportunities resulted from these high-dimensional data sets. Often data reduction or feature selection is the first step towards solving these massive data problems. However, data reduction through model selection or l_0 constrained optimization leads to combinatorial searches which are computationally expensive or infeasible for massive data problems. A computationally more efficient alternative to model selection is l_1 constrained optimization or Lasso optimization. In this talk, we will describe the Boosted Lasso (BLasso) algorithm that is able to produce an approximation to the complete regularization path for general Lasso problems. BLasso consists of both a forward step and a backward step. The forward step is similar to Boosting and Forward Stagewise Fitting, but the backward step is new and crucial for BLasso to approximate the Lasso path in all situations. For cases with finite number of base learners, when the step size goes to zero, the BLasso path is shown to converge to the Lasso path. Experimental results are also provided to demonstrate the difference between BLasso and Boosting or Forward Stagewise Fitting. We can extend BLasso to the case of a general convex loss penalized by a general convex function and illustrate this extended BLasso with examples. Since Lasso is used as a computationally more efficient alternative to model selection, it is important to study the model selection property of Lasso. I will present some (almost) necessary and sufficient conditions for Lasso to be model selection consistent in the classical case of small number of features and large sample size. (This is joint work with Peng Zhao at UC Berkeley.)
Views: 289 Microsoft Research
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: 2483 The Audiopedia
Mathematics of Machine Learning
 
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Do you need to know math to do machine learning? Yes! The big 4 math disciplines that make up machine learning are linear algebra, probability theory, calculus, and statistics. I'm going to cover how each are used by going through a linear regression problem that predicts the price of an apartment in NYC based on its price per square foot. Then we'll switch over to a logistic regression model to change it up a bit. This will be a hands-on way to see how each of these disciplines are used in the field. Code for this video (with coding challenge): https://github.com/llSourcell/math_of_machine_learning Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Sign up for the next course at The School of AI: http://theschool.ai/ More learning resources: https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/ https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning https://courses.washington.edu/css490/2012.Winter/lecture_slides/02_math_essentials.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693
Views: 126649 Siraj Raval
Lulu Qian - CS+Biology - Alumni College 2016
 
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"A Future Written by Molecular Programmers" Lulu Qian, Assistant Professor of Bioengineering, is interested in engineering molecular systems with intelligent behavior: specifically, exploring the principles of molecular programs in nature with the end goal of recreating synthetic molecular programs that approach the complexity and sophistication of life itself. To this end, she works on designing and constructing nucleic-acid systems from scratch that exhibit programmable behaviors from the basic level—such as recognizing molecular events from the environment, processing information, making decisions, and taking actions—to the advanced level, such as learning and evolving. The Caltech Alumni Association held a day-long event to explore the ways in which computational thinking at Caltech is disrupting science and engineering, creating entirely new disciplines with "CS+X". From developing new paradigms for computation—quantum computing and DNA computing—to pushing the boundaries of machine learning and statistics in ways that transform fields like astronomy, chemistry, neuroscience, and biology, Caltech faculty are pioneering new disciplines at the interface of computer science, and science and engineering. Learn more about the event - http://alumni.caltech.edu/alumni-college Produced in association with Caltech Academic Media Technologies. ©2016 California Institute of Technology
Views: 21172 caltech
Professor Brad Karp: "Safeguarding Users’ Sensitive Data in the Cloud and the Browser"
 
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The Turing Lectures: Computer Science - Professor Brad Karp: "Safeguarding Users’ Sensitive Data in the Cloud and the Browser" Click the below timestamps to navigate the video. 00:00:10 Welcome by Professor Jon Crowcroft 00:01:00 Speaker introduction by Professor Jon Crowcroft 00:02:21 Professor Brad Karp: "Safeguarding Users’ Sensitive Data in the Cloud and the Browser" 00:50:06 Q&A The final event in this first series of Turing Lectures is devoted to recent developments in Computer Science and their implications for Data Science. Professor Peter O’Hearn from both Facebook and UCL will talk about the challenges of managing large and evolving code bases that undergo rapid concurrent modification. Professor Brad Karp from UCL will discuss how sensitive data in the Cloud and Browser can be safeguarded by designing software adhering to the principle of least privilege, and does not divulge sensitive information, even when successfully exploited by an attacker. 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
Leda Braga: Data science and its role in investment strategy
 
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The CEO of Systematica Investments discusses how her company employs technology to achieve returns. As the first financial services industry speaker at the Women in Data Science (WiDS) conference, keynote presenter Leda Braga, CEO of Systematica Investments, introduced attendees to the role of data science applied to investment. Braga’s company manages large pools of assets from pension funds to insurance company premiums, to sovereign wealth funds of various countries. As a hedge fund manager, the company focuses on two areas of investment management – signal generation and portfolio construction. Signal generation is the process of trying to predict what assets should be bought or sold. Portfolio construction is the process of organizing investments strategically to meet investment objectives and achieve returns. Portfolio construction is essentially a constrained optimization problem, Braga notes, and as a result, it is an area where data science can help illuminate the best solutions. At Systematica Investments, the company uses data and algorithms to determine how to maximize financial returns given a number of variables with specific constraints within financial markets and environments. At the end of the day, she says, the business of investment management is the business of information management. Braga believes a passion for data science coupled with an interest in making a difference matters in the field of strategic investing. Worldwide, professionally managed assets are valued at $80 trillion. “If you want to change the world,” she says, “bank your money in the right places. And if you think about investment management as this activity whereby the pools of capital of the world get directed, that is so powerful. And if that is going to become completely data driven over time, you can’t miss that opportunity. You’ve got to join in and have a say.”
Professor Andrea Bertozzi: "Geometric Graph-Based Methods for High Dimensional Data"
 
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The Turing Lectures: The Intersection of Mathematics, Statistics and Computation - Professor Andrea Bertozzi: "Geometric Graph-Based Mathods for High Dimensional Data" Click the below timestamps to navigate the video. 00:00:11 Welcome & Introduction by Professor Jared Tanner 00:01:25 Professor Andrea Bertozzi: "Geometric Graph-Based Mathods for High Dimensional Data" 00:59:03 Q&A Lecture blurb: Geometric Graph-Based Mathods for High Dimensional Data We present new methods for segmentation of large datasets with graph based structure. The method combines ideas from classical nonlinear PDE-based image segmentation with fast and accessible linear algebra methods for computing information about the spectrum of the graph Laplacian. The goal of the algorithms is to solve semi-supervised and unsupervised graph cut optimization problems. I will present results for image processing applications such as image labelling and hyperspectral video segmentation, and results from machine learning and community detection in social networks, including modularity optimization posed as a graph total variation minimization problem. Bio: Andrea Bertozzi is an applied mathematician with expertise in nonlinear partial differential equations and fluid dynamics. She also works in the areas of geometric methods for image processing, crime modeling and analysis, and swarming/cooperative dynamics. Bertozzi completed all her degrees in Mathematics at Princeton. She was an L.E. Dickson Instructor and NSF Postdoctoral Fellow at the University of Chicago from 1991-1995. She was the Maria Geoppert-Mayer Distinguished Scholar at Argonne National Laboratory from 1995-6. She was on the faculty at Duke University from 1995-2004 first as Associate Professor of Mathematics and then as Professor of Mathematics and Physics. She has served as the Director of the Center for Nonlinear and Complex Systems while at Duke. Bertozzi moved to UCLA in 2003 as a Professor of Mathematics. Since 2005 she has served as Director of Applied Mathematics, overseeing the graduate and undergraduate research training programs at UCLA. In 2012 she was appointed the Betsy Wood Knapp Chair for Innovation and Creativity. Bertozzi’s honors include the Sloan Research Fellowship in 1995, the Presidential Early Career Award for Scientists and Engineers in 1996, and SIAM’s Kovalevsky Prize in 2009. She was elected to the American Academy of Arts and Sciences in 2010 and to the Fellows of the Society of Industrial and Applied Mathematics in 2010. She became a Fellow of the American Mathematical Society in 2013. 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.
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: 8160 systems biology
Susan Athey: Data science is about using data to answer questions and test hypotheses
 
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Susan Athey, the Economics of Technology Professor at Stanford Graduate School of Business, has always been interested in the intersection of economics and computer science. As an undergraduate she was a math, computer science and economics triple major. She explains that combining economics, social science, engineering and machine-learning tools allows you to answer questions in a way that wasn’t possible before.
A review of machine learning techniques for anomaly detection - Dr David Green
 
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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.
The Discipline of Organizing: The Intellectual Intersection of the Information Schools
 
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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.
«Mine Your Own Business» — Using Process Mining to Turn Big Data into Real Value
 
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http://0x1.tv/20131025-2A «Mine Your Own Business» — Using Process Mining to Turn Big Data into Real Value (Wil van der Aalst, SECR-2013) * Wil van der Aalst ------------- Recently, process mining emerged as a new scientific discipline on the interface between process models and event data. Conventional Business Process Management (BPM) and Workflow Management (WfM) approaches and tools are mostly model-driven with little consideration for event data. Data Mining (DM), Business Intelligence (BI), and Machine Learning (ML) focus on data without considering end-to-end process models. Process mining aims to bridge the gap between BPM and WfM on the one hand and DM, BI, and ML on the other hand. The challenge is to turn torrents of event data (“Big Data”) into valuable insights related to … xciting field that will become more and more important for the Information Systems (IS) discipline.
Views: 75 Stas Fomin
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 Stéphane Mallat: "High-Dimensional Learning and Deep Neural Networks"
 
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The Turing Lectures: Mathematics - Professor Stéphane Mallat: High-Dimensional Learning and Deep Neural Networks Click the below timestamps to navigate the video. 00:00:07 Welcome by Professor Andrew Blake, Director, The Alan Turing Institute 00:01:36 Introduction by Professor Jared Tanner 00:03:21 Professor Stéphane Mallat: High-Dimensional Learning and Deep Neural Networks 00:49:53 Q&A The first set of Turing Lectures took place on March 2 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. 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
Lecture: Mathematics of Big Data and Machine Learning
 
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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: 15683 MIT OpenCourseWare
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
Feature Selection Through Lasso
 
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Google Tech Talks November 21, 2006 ABSTRACT Information technology advances are making data collection possible in most if not all fields of science and engineering and beyond. Statistics as a scientific discipline is challenged and enriched by the new opportunities resulted from these high-dimensional data sets. Often data reduction or feature selection is the first step towards solving these massive data problems. However, data reduction through model selection or l_0 constrained least squares optimization leads to a combinatorial search which is computationally infeasible for massive data problems. A computationally efficient alternative is the l_1 constrained least squares optimization or...
Views: 3161 GoogleTechTalks
Lecture 01 - The Learning Problem
 
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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: 786945 caltech
Computational Biologist Prof. Burkhard Rost
 
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Burhard Rost is one of the researchers who has decisively shaped the development of bioinformatics into the indispensable discipline we know today. 2008 Rost came from Columbia University to Technical University Munich. A film of the Alexander von Humboldt foundation: http://www.humboldt-foundation.de German version of this video: http://youtu.be/ulCBxw-fikQ
Views: 5640 TUMuenchen1
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
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: 230337 caltech
See How an Insane 7-Circle Roundabout Actually Works | WIRED
 
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Your first thought upon seeing Swindon's 'magic roundabout' might be: man, the Brits have really lost the plot lately. But this thing—which is actually seven roundabouts in one—has been working for 60 years. Still haven’t subscribed to WIRED on YouTube? ►► http://wrd.cm/15fP7B7 ABOUT WIRED WIRED is where tomorrow is realized. Through thought-provoking stories and videos, WIRED explores the future of business, innovation, and culture. See How an Insane 7-Circle Roundabout Actually Works | WIRED
Views: 1147591 WIRED
queuing delay
 
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Views: 220 GATE STACK
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
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.
Ralf Herbrich: "Learning Real-World Probabilistic Models with Approximate Message Passing"
 
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The Turing Lectures: Industrial & Commercial - Ralf Herbrich – Amazon: Learning Real-World Probabilistic Models with Approximate Message Passing Click the below timestamps to navigate the video. 00:00:10 Introduction by Professor Chris Williams, Edinburgh University 00:01:19 Ralf Herbrich – Amazon: Learning Real-World Probabilistic Models with Approximate Message Passing Over the past few years, we have entered the world of big and structured data – a trend largely driven by the exponential growth of Internet-based online services such in Search, e-Commerce and Social Networking as well as the ubiquity of smart devices with sensors in everyday life. This poses new challenges for statistical inference and decision-making as some of the basic assumptions are shifting: (1) The ability to store the parameters of (data) models, (2) the level of granularity and ‘building blocks’ in the data modeling phase, and (3) the interplay of computation, storage, communication and inference and decision-making techniques. In this talk, I will discuss the implications of big and structured data for Statistics and the convergence of statistical model and distributed systems. I will present one of the most versatile modeling techniques that combines systems and statistical properties – factor graphs – and review a series of approximate inference techniques such as distributed message passing. The talk will be concluded with an overview of real-world problems at Amazon. 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
Intersections | 4 of 4  | Planned and Unplanned || Radcliffe Institute
 
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PLANNED AND UNPLANNED Official city plans are often not realized in their proposed forms, but rather adapt to realities on the ground, including unexpected practices and patterns of human behavior. Edgar Pieterse (08:11), South African research chair in urban policy and director, African Centre for Cities, University of Cape Town (South Africa) Ana Elvira Vélez Villa (26:58), architect (Colombia) Ricky Burdett (43:15), professor of urban studies and director, LSE Cities, the London School of Economics and Political Science Moderator: Eve Blau, co–principal investigator, Harvard-Mellon Urban Initiative; adjunct professor of the history and theory of urban form and design, Harvard Graduate School of Design PANEL DISCUSSION (1:06:09) AUDIENCE Q&A (1:22:19) CLOSING REMARKS (1:35:22) Julie A. Buckler
Views: 1014 Harvard University
21. Synthetic Biology: From Parts to Modules to Therapeutic Systems
 
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MIT 7.91J Foundations of Computational and Systems Biology, Spring 2014 View the complete course: http://ocw.mit.edu/7-91JS14 Instructor: Ron Weiss This guest lecture by Prof. Ron Weiss is on synthetic biology. Prof. Weiss describes how he came to be a synthetic biologist, followed by an overview of the field. He covers basic , technologies for scalability, and programmable therapeutics. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 7178 MIT OpenCourseWare
Interactive Data Analysis - Jeffrey Heer - May 23, 2013
 
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This talk is part of the symposium, "Data Visualization from Data to Discovery: Art Center + Caltech + JPL", May 23, 2013 | Beckman Auditorium | Caltech, Pasadena, CA, USA | http://www.hi.jpl.nasa.gov/datavis Interactive Data Analysis Data analysis is a complex process with frequent shifts among data formats and models, and among textual and graphical media. We are investigating how to better support this lifecycle of analysis by identifying critical bottlenecks and developing new methods at the intersection of data visualization, machine learning and computer systems. Can we empower users to transform and clean data without programming? Can we design scalable representations and systems to visualize and query big data in real-time? How might we enable domain experts to guide machine learning methods to produce better models? Jeffrey Heer presents selected projects that attempt to address these challenges and introduce new tools for interactive visual analysis. Jeffrey Heer is an Assistant Professor of Computer Science at Stanford University, where he works on human-computer interaction, visualization and social computing. His research investigates the perceptual, cognitive and social factors involved in making sense of large data collections, resulting in new interactive systems for visual analysis and communication. The visualization tools developed by his lab (D3, Protovis, Flare, Prefuse) are used by researchers, companies and thousands of data enthusiasts around the world. His group has received Best Paper and Honorable Mention awards at the premier venues in Human-Computer Interaction and Information Visualization (ACM CHI, ACM UIST, IEEE InfoVis, IEEE VAST). In 2009 Jeff was named to MIT Technology Review's TR35; in 2012 he was named a Sloan Foundation Research Fellow. He holds BS, MS and PhD degrees in Computer Science from the University of California, Berkeley. About the symposium: Nearly every scientific and engineering endeavor faces a fundamental challenge to see and extract insights from data. Effective Data Science and Visualization can lead to new discoveries. Together, we at Caltech, NASA JPL, and Art Center represent the same convergence of science, engineering and design that drives new Big Data-powered discovery. On May 23, 2013, industry leaders visited Pasadena for a series of talks to inspire, unite and challenge our community to re-examine our practices, and our perspectives. Guests included: * Fernanda Viégas & Martin Wattenberg | Co-leaders, Google Data Visualization Group * Jer Thorp | Co-founder, The Office for Creative Research * Golan Levin | Director, Carnegie Mellon Studio for Creative Inquiry * Eric Rodenbeck | Founder, Stamen Design * Jeff Heer | Assistant Professor, Stanford University * Anja-Silvia Goeing | Privatdozent, University of Zurich and Lecturer of History and History of Science, Caltech See http://www.hi.jpl.nasa.gov/datavis for more information. Produced in association with Caltech Academic Media Technologies. © 2013 California Institute of Technology.
Views: 11774 caltech
Professor Peter O’Hearn: "Reasoning with Big Code"
 
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The Turing Lectures: Computer Science - Professor Peter O’Hearn: "Reasoning with Big Code" Click the below timestamps to navigate the video. 00:00:10 Welcome by Professor Jon Crowcroft 00:02:51 Speaker introduction by Professor Jon Crowcroft 00:03:38 Professor Peter O’Hearn: "Reasoning with Big Code" 00:43:42 Q&A The final event in this first series of Turing Lectures is devoted to recent developments in Computer Science and their implications for Data Science. Professor Peter O’Hearn from both Facebook and UCL will talk about the challenges of managing large and evolving code bases that undergo rapid concurrent modification. Professor Brad Karp from UCL will discuss how sensitive data in the Cloud and Browser can be safeguarded by designing software adhering to the principle of least privilege, and does not divulge sensitive information, even when successfully exploited by an attacker. 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 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.”
Professor Bin Yu: "Unveiling the Mysteries in Spatial Gene Expression"
 
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The Turing Lectures: Statistics - Professor Bin Yu: "Unveiling the Mysteries in Spatial Gene Expression" Click the below timestamps to navigate the video. 00:00:07 Introduction by Professor Sofia Olhede 00:01:18 Professor Bin Yu: "Unveiling the Mysteries in Spatial Gene Expression" 00:55:40 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. 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
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: 64658 Itronix Solution
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."
Managing the Machines: a year in review
 
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As a companion piece to her launch talk one year ago, Distinguished Professor Genevieve Bell presents this public lecture highlighting what has changed over the last 12 months and charting the 3A Institute progress towards its goals. She announces an exciting new initiative and the 3A Institute's new ambitious program for impact. The 3A Institute IoT turbo-charged by AI, advanced robotics, automated vehicles incorporating sensing data with other datasets, powerful big data analytics like machine learning driving automated decision-making. However you choose to paint the picture, the collection of technologies we are currently calling artificial intelligence (AI) is heralding the next industrial revolution. Some of these tools are new, some have been refined over preceding decades. However, we are now seeing their rapid convergence into systems - cyber-physical systems - that will have an unprecedented impact on humanity through deep economic, social and cultural shifts. To face these challenges head-on, the ANU created the Innovation Institutes framework. 12 months ago, Distinguished Professor Genevieve Bell founded the 3A Institute - the first of the Innovation Institutes - with the mission of creating a new applied science in an accelerated timeframe. Through the vehicle of the 3A Institute, the ANU will ultimately educate a new cohort of people who can critically examine and manage technological constellations - these cyber-physical systems - through the life-cycle from design to deployment to de-commissioning. Distinguished Professor Genevieve Bell Professor Bell is the Director of the 3A Institute, Florence Violet McKenzie Chair, and a Distinguished Professor at the ANU as well as a Vice President and Senior Fellow at Intel Corporation. Prof Bell is a cultural anthropologist, technologist and futurist best known for her work at the intersection of cultural practice and technology development.
Views: 335 ANU TV
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: 108697 Google Developers
Federico Echenique - CS+Economics - Alumni College 2016
 
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"Algorithms in Economics" Federico Echenique, the Allen and Lenabelle Davis Professor of Economics and Executive Officer for the Social Sciences, studies economic models of agents and markets to understand and determine their testable implications and the relationships between different theoretical constructs. In particular, he’s investigating models of individual decision-making, of markets, and of other economic institutions. Echenique conducts research at the intersection of economics and computer science, in particular studying algorithmic solution to centralized markets. Examples of such markets are the assignment of medical school graduates to hospitals, and children to schools. Echenique’s research also seeks to understand the computational assumptions and requirements behind many of the mathematical models used by economists. The Caltech Alumni Association held a day-long event to explore the ways in which computational thinking at Caltech is disrupting science and engineering, creating entirely new disciplines with "CS+X". From developing new paradigms for computation—quantum computing and DNA computing—to pushing the boundaries of machine learning and statistics in ways that transform fields like astronomy, chemistry, neuroscience, and biology, Caltech faculty are pioneering new disciplines at the interface of computer science, and science and engineering. Learn more about the event - http://alumni.caltech.edu/alumni-college Produced in association with Caltech Academic Media Technologies. ©2016 California Institute of Technology
Views: 626 caltech
Tim Harris: "Systems Challenges in Graph Analytics"
 
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The Turing Lectures: Industrial & Commercial - Tim Harris – Oracle Laboratories: Systems Challenges in Graph Analytics Click the below timestamps to navigate the video. 00:00:10 Introduction by Professor Chris Williams, Edinburgh University 00:01:49 Tim Harris – Oracle Laboratories: Systems Challenges in Graph Analytics 00:50:51 Q&A Graphs are at the core of many data processing problems, whether that is searching through billions of records for suspicious interactions, ranking the importance of web pages based on their connectivity, or identifying possible “missing” friends on a social network. This talk will discuss the challenges in building large, scalable, in-memory graph analytics systems. Many of these challenges come from the way that graph algorithms behave differently based on the structure of the input graph: a planar road network graph can produce a significantly different load on the machine’s memory system from a low-diameter social network graph. It can be necessary to select particular algorithms for these different cases, and to make contrasting decisions over how the machine’s resources are allocated. Finally, we face challenges simply from the scale at which we operate: making efficient use of the hardware in new SPARC machines with over 4000 threads. 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
Robert Ghrist: The Power of Online Learning
 
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A revolution in online learning, “Calculus: Single Variable,” Robert Ghrist’s popular Coursera course, contains 16 hours of hand-drawn video, and has attracted more than 130,000 students from all over the world. In 2013, it also became one of the first Coursera offerings approved for accreditation by the American Council on Education. Ghrist, the Andrea Mitchell University Professor of Mathematics and Electrical & Systems Engineering with appointments in Penn’s School of Arts & Sciences and the School of Engineering and Applied Science, works at the intersection of applied mathematics, systems engineering, and topology—the mathematical study of abstract space. The result brings a theoretical area of study off the page and into the real world. Ghrist is one of 15 Penn Integrates Knowledge University Professors, a group of researchers and scholars whose world-renowned work bridges multiple academic disciplines.
Running a Data Science Laboratory: Adventures of a Network Biologist
 
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Dr. Trey Ideker from University of California, San Diego presents a lecture titled "Running a Data Science: Adventures of a Network Biologist. Lecture Abstract Data sciences are playing an ever increasing role in biology and medicine. Nonetheless, building a research laboratory focused on data science involves special and important considerations not experienced in other biomedical disciplines. This lecture will coverr some of these considerations and ways to navigate them, with anecdotes from Dr. Ideker's own research program in Network Biology. View slides https://drive.google.com/open?id=0B4IAKVDZz_JUNXBPNUpMRFRSUDg About the Speaker Trey Ideker, Ph.D. is a Professor of Genetics and Bioengineering in the Department of Medicine at the University of California at San Diego. He serves as Director of the National Resource for Network Biology, Director of the San Diego Center for Systems Biology, and Co-Director of the Cancer Cell Mapping Initiative. Additionally, he is an Adjunct Professor of Computer Science and Bioengineering, and a member of the Moores Cancer Center at UC San Diego. Dr. Ideker received his Bachelor’s and Master’s degrees from MIT in Electrical Engineering and Computer Science and his Ph.D. from the University of Washington in Molecular Biology under the supervision of Dr. Leroy Hood. Dr. Ideker has founded influential bioinformatic tools including Cytoscape, a popular network analysis platform which has been cited over 12,000 times. Ideker serves on the Editorial Boards for Cell, Cell Reports, Nature Scientific Data, EMBO Molecular Systems Biology, and PLoS Computational Biology and is a Fellow of AAAS and AIMBE. He was named one of the Top 10 Innovators of 2006 by Technology Review magazine and was the recipient of the 2009 Overton Prize from the International Society for Computational Biology. His work has been featured in news outlets such as The Scientist, the San Diego Union Tribune, Forbes magazine, and the New York Times. Join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
Stanford Webinar: Common Pitfalls of A/B Testing and How to Avoid Them
 
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A Stanford Webinar presented by the Stanford Leadership & Management Science (http://stanford.io/2ppiCxy) and Decision Analysis (http://stanford.io/2pByYDC) graduate certificate programs "Common Pitfalls of A/B Testing and How to Avoid Them" Speaker: Ramesh Johari, Stanford University A/B testing is the process of using randomized controlled experiments on a web page or technology platform to determine which of two versions has a higher conversion rate. While this method of testing has become ubiquitous, there remain some common traps to which even the most sophisticated data scientists fall prey. Join Ramesh Johari, Associate Professor, Stanford Department of Management Science & Engineering, as he discusses some common practices and pitfalls in A/B testing. You will learn: -To analyze the output of A/B tests -To balance quick results with statistical significance during continuous monitoring -To maintain data integrity when running simultaneous experiments About the Speaker: Ramesh Johari is interested in the design and management of large-scale complex networks, such as the Internet. Using tools from operations research, engineering, and economics, he has developed models to analyze efficient market mechanisms for resource allocation in networks.
Views: 2086 stanfordonline
Yale Day of Data 2015: Chaitan Baru, “Data Science R&D: Current Activities, Future Directions"
 
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Chaitan Baru, Senior Advisor for Data Science, CISE Directorate, National Science Foundation, gives a keynote presentation for the Yale Day of Data 2015, with an introduction by Yale University President, Peter Salovey, on September 18, 2015. For more information about the Day of Data, visit http://elischolar.library.yale.edu/dayofdata/
Views: 499 YaleUniversity
What Is A Computer Engineer?
 
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Computer engineering? Live sciencecomputer engineering degrees & careers. Uh computer (systems) engineering cidse@asu 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
Loud Luxury feat. brando - Body (Official Lyric Video)
 
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Stream Loud Luxury – Body now on Spotify: https://open.spotify.com/track/21RzyxY3EFaxVy6K4RqaU9?si=lsH7KDxqQxyi2G6w-qqAiw 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: 47912364 Armada Music
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: info@allatra.tv
Views: 851229 АллатРа ТВ
The future of artificial intelligence and self-driving cars
 
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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.”
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: 1840 Talks at Google