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A search of MERLOT materialsCopyright 1997-2015 MERLOT. All rights reserved.Tue, 21 Apr 2015 06:47:37 PDTTue, 21 Apr 2015 06:47:37 PDTMERLOT Search - category=2787&keywords=mathematicshttp://www.merlot.org:80/merlot/images/merlot.gif
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4434(e) Science News
http://www.merlot.org/merlot/viewMaterial.htm?id=891431
(e) Science News uses a fully-automated, artificial intelligence program to sort the daily news you find on Yahoo, Bing, Google, and science websites into schema groups for you to read. The schema groups are: Astronomy/Space, Biology/Nature, Environment/ Climate, Health/Medicine, Economics/Math, Paleontology/Archaeology, Physics/ Chemistry, and Psychology/ Sociology. It also archives these daily news articles and newsfeeds by day/month/year. Note: It a newsfeed is missing from a past news article you can search for it at video repositories, such as www.blinkx.com and www.youtube. Electronic Journals Library
http://www.merlot.org/merlot/viewMaterial.htm?id=300557
University Library of Regensburg offers the "Electronic Journals Library," which contains over 25,000 titles, of which over 10,500 journals can be read free-of-charge. There are journal articles in the following areas: Agriculture, Archeaology, Civil engineering, Biology, Chemistry, Art, Computer Science, Education, Economics, History, Mathematics, Media, Medicine, Linguistics, Philosophy, Sociology, Science,Technology and many more. For more information about Electronic Journals Library see: http://rzblx1.uni-regensburg.de/ezeit/index.phtml?bibid=AAAAA&colors=7&lang=en.SIMPLE-Science- Lessons
http://www.merlot.org/merlot/viewMaterial.htm?id=379049
This website has great resources for Lesson Topics and examples for Earth and Space Science, Biology, Medicine, Mathematics, Social Sciences, and Digital Literacy. "Ideal" Language Learning and the Psychological Resource Problem
http://www.merlot.org/merlot/viewMaterial.htm?id=973026
This video was recorded at MIT World Series: Where Does Syntax Come From? Have We All Been Wrong?. Some linguists study what can be learned in principle, but William Gregory Sakas asks "the feasibility question -- how efficient learning takes place." This talk focuses on such research, its historical antecedents, and issues that trouble Sakas and his colleagues. Sakas provides a swift conceptual survey of modeling parameter-setting, from Chomsky, through Yang and Lightfoot. In "the spirit of Pinker," Sakas believes that any computational model of a feasible learner must be compatible with the psychological resources of a human child. So Sakas's lab tries to zero in on "what is needed in the way of psycho-computational resources in a learner to converge on the target grammar on the basis of a limited sample of sentences." The lab has created "a large, artificial but linguistically motivated domain of parameterized languages for evaluating learning models," with more than 1.6 million parsed sentences. Sakas and his colleagues compare the efficiencies of different parameter-setting models, attempt to solve such modeling problems as noise and over-generalization, as well as evaluate richness of the stimulus claims. Underlying this work – "the whole parameter setting enterprise" -- says Sakas, is the effort "to limit the resources to what can reasonably be attributed to young children," so as to reduce the complexity of innate knowledge -- generally limit the amount and complexity of input and processing of each sentence. The sticky problem remains of finding a "psycho-computationally palatable way" of modeling a process of fitting grammar to multiple sentences. "We feel alone in this endeavor," says Sakas. While there's lots of interesting recent work on modeling syntax acquisition, mathematical, and statistical/probabilistic learning, "we haven't been able to take it into our models, because it doesn't seem to be concerned with resource limits." The ideal learner other linguists discuss "is not a learner concerned with resource issues." Sakas asks if "this work is intended to mirror psychological reality." He notes, "The effective richness of the stimulus for a child language learner depends on the child's non-ideal capacity to extract information from heard utterances." The point is to model what a child really does with the linguistic input available to her. Sakas concludes with a question: "Is there anyone out there trying to solve the psychological resource problem?15.761 Introduction to Operations Management (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=884692
This course provides students with concepts, techniques and tools to design, analyze, and improve core operational capabilities, and apply them to a broad range of application domains and industries. It emphasizes the effect of uncertainty in decision-making, as well as the interplay between high-level financial objectives and operational capabilities. Topics covered include production control, risk pooling, quality management, process design, and revenue management. Also included are case studies, guest lectures, and simulation games which demonstrate central concepts.17.872 Quantitative Research in Political Science and Public Policy
http://www.merlot.org/merlot/viewMaterial.htm?id=554913
This course provides students with a rigorous introduction to Statistics for Political Science. Topics include basic mathematical tools used in social science modeling and statistics, probability theory, theory of estimation and inference, and statistical methods, especially differences of means and regression. The course is often taken by students outside of political science, especially those in business, urban studies, and various fields of public policy, such as public health. Examples draw heavily from political science, but some problems come from other areas, such as labor economics.9.29J / 9.912J / 8.261J Introduction to Computational Neuroscience
http://www.merlot.org/merlot/viewMaterial.htm?id=554684
This course gives a mathematical introduction to neural coding and dynamics. Topics include convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, and reinforcement learning. Applications to neural coding, focusing on the visual system are covered, as well as Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission. Visit the Seung Lab Web site.A Beautiful Mind: Genius, Madness, Reawakening
http://www.merlot.org/merlot/viewMaterial.htm?id=972808
This video was recorded at MIT World Series: Applied Mathematics Colloquium. Dr. Sylvia Nasar, the author of "A Beautiful Mind" tells the extraordinary story of mathematician John Nash a drama about the mystery of the human mind and shares some of her experiences in writing her prize-winning biography.A blind deconvolution method for neural spike identification
http://www.merlot.org/merlot/viewMaterial.htm?id=975605
This video was recorded at Video Journal of Machine Learning Abstracts - Volume 2. We consider the problem of estimating neural spikes from extracellular voltage recordings. Most current methods are based on clustering, which requires substantial human supervision and produces systematic errors by failing to properly handle temporally overlapping spikes. We formulate the problem as one of statistical inference, in which the recorded voltage is a noisy sum of the spike trains of each neuron convolved with its associated spike waveform. Joint maximum-a-posteriori (MAP) estimation of the waveforms and spikes is then a blind deconvolution problem in which the coefficients are sparse. We develop a block-coordinate descent method for approximating the MAP solution. We validate our method on data simulated according to the generative model, as well as on real data for which ground truth is available via simultaneous intracellular recordings. In both cases, our method substantially reduces the number of missed spikes and false positives when compared to a standard clustering algorithm, primarily by recovering temporally overlapping spikes. The method offers a fully automated alternative to clustering methods that is less susceptible to systematic errors.A New Kind of Science
http://www.merlot.org/merlot/viewMaterial.htm?id=973416
This video was recorded at MIT World Series: Applied Mathematics Colloquium. Wouldn't it be exciting, Stephen Wolfram wonders, to have a little computer program that could function as a precise, ultimate model of our universe? If you ran the program long enough, it would reproduce every single thing that happens. It's not out of the question, according to Wolfram's lecture which somehow encapsulates his 1,200-page opus, A New Kind of Science, in a single hour. Wolfram's vast and penetrating research uses simple computations to generate complex computer models that resemble designs found in nature. He embraces the really big subjects, and the really small ones—from patterns on mollusk shells and the shapes of leaves and snowflakes, to free will, evolution, and extra-terrestrial life. This new kind of thinking might provide alternatives to evolution in explaining how different forms of life emerged. Wolfram believes his work is already transforming the study of science, as well as making possible a host of new technologies.