This lecture provides an introduction to entropy in general, and multi-scale entropy (MSE) in particular, highlighting the potential clinical applications of the latter.

Difficulty level: Intermediate

Duration: 39:05

Speaker: : Jil Meier

In this lecture, you will learn about various neuroinformatic resources which allow for 3D reconstruction of brain models.

Difficulty level: Intermediate

Duration: 1:36:57

Speaker: : Michael Schirner

This lecture on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about.

Difficulty level: Beginner

Duration: 27:48

Speaker: : Gunnar Blohm

Course:

This lecture focuses on how to get from a scientific question to a model using concrete examples. We will present a 10-step practical guide on how to succeed in modeling. This lecture contains links to 2 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded Q&A sessions.

Difficulty level: Beginner

Duration: 29:52

Speaker: : Megan Peters

Course:

This lecture focuses on the purpose of model fitting, approaches to model fitting, model fitting for linear models, and how to assess the quality and compare model fits. We will present a 10-step practical guide on how to succeed in modeling.

Difficulty level: Beginner

Duration: 26:46

Speaker: : Jan Drugowitsch

This lecture provides an overview of the generalized linear models (GLM) course, originally a part of the Neuromatch Academy (NMA), an interactive online summer school held in 2020. NMA provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience".

Difficulty level: Beginner

Duration: 33:58

Speaker: : Cristina Savin

In this lesson you will learn about the Bayesian Virtual Epileptic Patient (BVEP), a research use case using TVB supported on the EBRAINS infrastructure.

Difficulty level: Intermediate

Duration: 15:39

Speaker: : Meysam Hashemi

Course:

This lecture introduces the core concepts of dimensionality reduction.

Difficulty level: Beginner

Duration: 31:43

Speaker: : Byron Yu

Course:

This is the first of a series of tutorials on fitting models to data. In this tutorial, we start with simple linear regression, using least squares optimization.

Difficulty level: Beginner

Duration: 6:18

Speaker: : Anqi Wu

Course:

In this tutorial, we will use a different approach to fit linear models that incorporates the random 'noise' in our data.

Difficulty level: Beginner

Duration: 8:00

Speaker: : Anqi Wu

Course:

This tutorial discusses how to gauge how good our estimated model parameters are.

Difficulty level: Beginner

Duration: 5:00

Speaker: : Anqi Wu

Course:

In this tutorial, we will generalize the regression model to incorporate multiple features.

Difficulty level: Beginner

Duration: 7:50

Speaker: : Anqi Wu

Course:

This tutorial teaches users about the bias-variance tradeoff and see it in action using polynomial regression models.

Difficulty level: Beginner

Duration: 6:38

Speaker: : Anqi Wu

Course:

This tutorial covers how to select an appropriate model based on cross-validation methods.

Difficulty level: Beginner

Duration: 5:28

Speaker: : Anqi Wu

This is a tutorial covering Generalized Linear Models (GLMs), which are a fundamental framework for supervised learning. In this tutorial, the objective is to model a retinal ganglion cell spike train by fitting a temporal receptive field: first with a Linear-Gaussian GLM (also known as ordinary least-squares regression model) and then with a Poisson GLM (aka "Linear-Nonlinear-Poisson" model). The data you will be using was published by Uzzell & Chichilnisky 2004.

Difficulty level: Beginner

Duration: 8:09

Speaker: : Anqi Wu

This tutorial covers the implementation of logistic regression, a special case of GLMs used to model binary outcomes. In this tutorial, we will decode a mouse's left/right decisions from spike train data.

Difficulty level: Beginner

Duration: 6:42

Speaker: : Anqi Wu

Course:

This tutorial covers multivariate data can be represented in different orthonormal bases.

Difficulty level: Beginner

Duration: 4:48

Speaker: : Alex Cayco Gajic

Course:

This tutorial covers how to perform principal component analysis (PCA) by projecting the data onto the eigenvectors of its covariance matrix.

To quickly refresh your knowledge of eigenvalues and eigenvectors, you can watch this short video (4 minutes) for a geometrical explanation. For a deeper understanding, this in-depth video (17 minutes) provides an excellent basis and is beautifully illustrated.

Difficulty level: Beginner

Duration: 6:33

Speaker: : Alex Cayco Gajic

Course:

This tutorial covers how to apply principal component analysis (PCA) for dimensionality reduction, using a classic dataset that is often used to benchmark machine learning algorithms: MNIST. We'll also learn how to use PCA for reconstruction and denoising.

You can learn more about MNIST dataset here.

Difficulty level: Beginner

Duration: 5:35

Speaker: : Alex Cayco Gajic

Course:

This tutorial covers how dimensionality reduction can be useful for visualizing and inferring structure in your data. To do this, we will compare principal component analysis (PCA) with t-SNE, a nonlinear dimensionality reduction method.

Difficulty level: Beginner

Duration: 4:17

Speaker: : Alex Cayco Gajic

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