|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/dimension-reduction-and-source-separation-in-neuroscience/
课程评论：没有评论
课程名称：主成分分析与多变量信号处理，应用于神经数据 课程概述： 本课程聚焦于神经科学的最新发展，尤其是如何利用新兴的脑成像技术分析不断增长的数据集。现代神经科学面临的主要挑战之一是如何有效分析这些“大数据”。由于同时记录的数据通道数量不断增加，我们能够发现大脑中的时空结构，但这也带来了数据分析的新挑战。鉴于数据以矩阵形式存储，线性代数中开发的算法非常有用。 本课程旨在教授一些基于矩阵的数据分析方法，特别强调多变量维度降低和源分离技术，包括协方差矩阵、主成分分析（PCA）、广义特征分解（比PCA更优秀的技术！）和独立成分分析（ICA）。课程内容数学严谨，但对于没有正式数学背景的学员也十分友好。课程提供MATLAB和Python代码（视频中展示的是MATLAB代码，Python代码与之相匹配）。 您适合参加本课程如果您是： - 寻找分析多变量数据方法的神经科学研究者 - 希望在神经科学博士或博士后职位上具备竞争力的学生 - 对现代脑科学中的关键问题感兴趣的非神经科学人士 - 期望提高线性代数知识的独立学习者 - 对神经科学中的应用矩阵分解感到好奇的数学家、工程师或物理学家 - 希望深入了解主成分分析（PCA）或独立成分分析（ICA）的人 - 对课程预览中引导图像感到好奇并想要了解其含义的人（答案就在课程中！） 如果您不确定这门课程是否适合您，欢迎查看预览视频，并随时与我联系以获取更多信息。期待在课程中见到您！
What is this course all about?Neuroscience (brain science) is changing - new brain-imaging technologies are allowing increasingly huge data sets, but analyzing the resulting Big Data is one of the biggest struggles in modern neuroscience (if don't believe me, ask a neuroscientist!). The increases in the number of simultaneously recorded data channels allows 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. The course comes with MATLAB and Python code (note that the videos show the MATLAB code and the Python code is a close match).You should take this course if you are a...neuroscience researcher who is looking for ways to analyze your multivariate data.student who wants to be competitive for a neuroscience PhD or postdoc position.non-neuroscientist who is interested 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?I worked hard to make this course accessible to anyone with at least minimal linear algebra and programming background. But this course is not right for everyone. Check out the preview videos and feel free to contact me if you have any questions.I look forward to seeing you in the course!
