Udemy – PCA & multivariate signal processing, applied to neural data 2020-8/2021-2

Description

PCA & multivariate signal processing, applied to neural data, PCA core component analysis and multi-component signal processing can be used for neural network data provided by Udemy. This course teaches you theoretically the techniques of analyzing and cutting big data in neuroscience. You also put the tutorials into practice by learning how to code in the article.

The aim of this training course is to teach matrix-based data analysis methods for time series data in neuroscience with emphasis on multivariate reduction methods and source-separation.

The trainings provided include covariance matrix PCA principal component analysis, special vector composition, and independent component analysis. However, this course covers difficult math topics. But it can also be used for people who do not have an academic background in mathematics. Content software is used as a numerical data processing engine.

Items that are taught in this course

Learn advanced methods of linear algebra

Application of advanced methods of linear algebra in MATLAB software

Multivariate data simulation to test analysis methods

Multivariate time series data sets analysis

Familiarity with the challenges facing neuroscience

Learn modern neuroscience data analysis

Course information

Publisher:: Udemy Instructor: Mike X Cohen Duration: 10h 4m Number of lessons: 80 lessons English language

Course Prerequisites

Some linear algebra background (or interest in learning!) Some neuroscience background (or interest in learning!) Some MATLAB programming experience (only to complete exercises) Interest in learning applied linear algebra

Installation

After Extract, watch with your favorite Player.

English subtitle

Quality: 720p

Changes:

Version 2021/2 has not changed in the number of courses and time compared to 2020/8 and has not been updated.