PCA & multivariate signal processing, applied to neural data | Science Online Course by Udemy

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Learn and apply cutting-edge data analysis techniques for the age of “big data” in neuroscience (theory and MATLAB code)

An excellent training about Science

PCA & multivariate signal processing, applied to neural data

What is thiscourse all about?Neuroscience(brain science)is changing – new brain-imagingtechnologies are allowing increasingly huge data sets, but analyzingthe resulting Big Datais one of the biggest struggles in modern neuroscience (ifdon’t believe me, ask a neuroscientist!). The increases in the number of simultaneously recorded data channelsallows new discoveries about spatiotemporal structure in the brain, but also presents new challenges for data analyses. Because data are stored in matrices, algorithms developed in linear algebra are extremely useful. The purpose of this course is to teach you some matrix-based data analysis methods in neural time series data, with a focus on multivariate dimensionality reduction and source-separation methods. This includes covariance matrices, principal components analysis (PCA), generalized eigendecomposition (even better than PCA!), and independent components analysis (ICA). The course is mathematically rigorous but is approachable to individuals with no formal mathematics background. MATLAB is the primary numerical processing engine but the material is easily portable to Python or any other language. You should take this course if you are a.neuroscienceresearcherwho is looking for ways to analyze your multivariatedata. studentwho wants to becompetitive for a neuroscience PhD or postdoc position. non-neuroscientistwho isinterested in learning more about the big questions in modern brain science. independent learner who wants to advance your linear algebra knowledge. mathematician, engineer, or physicist who is curious about applied matrix decompositions in neuroscience. person who wants to learn more about principal components analysis (PCA) and/or independent components analysis (ICA)intrigued by the image that starts off the Course Preview and want to know what it means! (The answers are in this course!)Unsure if this course is right for you?Iworked hard to make this course accessible to anyone with at least minimal linear algebra and programming background. But this courseis not right for everyone. Check out the preview videos and feel free tocontact me if you have any questions. Ilook forward to seeing you in the course!

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