This tutorial walks through several common methods for visualizing your data in different ways depending on your data type.
This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.
This brief tutorial goes over how you can easily work with big data as you would with any size of data.
In this tutorial, you will learn how to deploy your models outside of your local MATLAB environment, enabling wider sharing and collaboration.
This lecture on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about.
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.
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.
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".
This lecture introduces the core concepts of dimensionality reduction.
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.
In this tutorial, we will use a different approach to fit linear models that incorporates the random 'noise' in our data.
This tutorial discusses how to gauge how good our estimated model parameters are.
In this tutorial, we will generalize the regression model to incorporate multiple features.
This tutorial teaches users about the bias-variance tradeoff and see it in action using polynomial regression models.
This tutorial covers how to select an appropriate model based on cross-validation methods.
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.
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.
This tutorial covers multivariate data can be represented in different orthonormal bases.
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.
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.