Tutorial 2 Linear Regression With Mle Neuromatch Academy

Tutorial 2 Linear Regression With Mle Neuromatch Academy This is tutorial 2 of a series on fitting models to data. we start with simple linear regression, using least squares optimization (tutorial 1) and maximum likelihood estimation (tutorial 2). we will use bootstrapping to build confidence intervals around the inferred linear model parameters (tutorial 3). Tutorial objectives. estimated timing of tutorial: 30 minutes. this is tutorial 2 of a series on fitting models to data. we start with simple linear regression, using least squares optimization (tutorial 1) and maximum likelihood estimation (tutorial 2). we will use bootstrapping to build confidence intervals around the inferred linear model.

Tutorial 2 Linear Regression With Mle Neuromatch Academy Estimated timing of tutorial: 30 minutes. this is tutorial 1 of a series on fitting models to data. we start with simple linear regression, using least squares optimization (tutorial 1) and maximum likelihood estimation (tutorial 2). we will use bootstrapping to build confidence intervals around the inferred linear model parameters (tutorial 3). Welcome to the comp neuro course! we have curated a curriculum that spans most areas of computational neuroscience (a hard task in an increasingly big field!). we will expose you to both theoretical modeling and more data driven analyses. this section will overview the curriculum. we will start with several optional pre reqs refreshers. Estimated timing of tutorial: 45 minutes. in this tutorial, we will look at the dynamical systems introduced in the first tutorial through a different lens. in tutorial 1, we studied dynamical systems as a deterministic process. for tutorial 2, we will look at probabilistic dynamical systems. you may sometimes hear these systems called stochastic. This is tutorial 4 of a series on fitting models to data. we start with simple linear regression, using least squares optimization (tutorial 1) and maximum likelihood estimation (tutorial 2). we will use bootstrapping to build confidence intervals around the inferred linear model parameters (tutorial 3).

Tutorial 2 Linear Regression With Mle Neuromatch Academy Estimated timing of tutorial: 45 minutes. in this tutorial, we will look at the dynamical systems introduced in the first tutorial through a different lens. in tutorial 1, we studied dynamical systems as a deterministic process. for tutorial 2, we will look at probabilistic dynamical systems. you may sometimes hear these systems called stochastic. This is tutorial 4 of a series on fitting models to data. we start with simple linear regression, using least squares optimization (tutorial 1) and maximum likelihood estimation (tutorial 2). we will use bootstrapping to build confidence intervals around the inferred linear model parameters (tutorial 3). This is tutorial 1 of a series on fitting models to data. we start with simple linear regression, using least squares optimization (tutorial 1) and maximum likelihood estimation (tutorial 2). we will use bootstrapping to build confidence intervals around the inferred linear model parameters (tutorial 3). Tutorial 1: linear regression with mse; tutorial 2: linear regression with mle; tutorial 3: confidence intervals and bootstrapping; tutorial 4: multiple linear regression and polynomial regression; tutorial 5: model selection: bias variance trade off; tutorial 6: model selection: cross validation; outro; suggested further readings; day summary.