A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that 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 on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about. This lecture contains links to 3 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded question and answer sessions.

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that 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 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 question and answer sessions.

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that 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 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 is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that 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 provides an overview of generalized linear models (GLM) and contains links to 2 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded question and answer sessions.

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that 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 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). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

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). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

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

This is Tutorial 3 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). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

In this tutorial, we will discuss how to gauge how good our estimated model parameters are.

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). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

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

This is Tutorial 5 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). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

In this tutorial, we will learn about the bias-variance tradeoff and see it in action using polynomial regression models.

This is Tutorial 6 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). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

This tutorial covers 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). This tutorial also covers a special case of GLMs, logistic regression, and learn how to ensure good model performance. This tutorial is designed to run with retinal ganglion cell spike train data from Uzzell & Chichilnisky 2004.

This tutorial covers the implementation of logistic regression, a special case of GLMs used to model binary outcomes. Oftentimes the variable you would like to predict takes only one of two possible values. Left or right? Awake or asleep? Car or bus? In this tutorial, we will decode a mouse's left/right decisions from spike train data.

Objectives of this tutorial:

1. Learn about logistic regression, how it is derived within the GLM theory, and how it is implemented in scikit-learn
2. Apply logistic regression to decode choices from neural responses
3. Learn about regularization, including the different approaches and the influence of hyperparameters

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

Overview of this tutorial:

• Generate correlated multivariate data
• Define an arbitrary orthonormal basis
• Project the data onto the new basis

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

Overview of this tutorial:

• Calculate the eigenvectors of the sample covariance matrix.
• Perform PCA by projecting data onto the eigenvectors of the covariance matrix.
• Plot and explore the eigenvalues.

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.

Overview of this tutorial:

• Perform PCA on MNIST
• Calculate the variance explained
• Reconstruct data with different numbers of PCs
• (Bonus) Examine denoising using PCA

You can learn more about MNIST dataset here.

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.

Overview of the tutorial:

• Visualize MNIST in 2D using PCA
• Visualize MNIST in 2D using t-SNE

This tutorial provides an introduction to Bayesian statistics and covers developing a Bayesian model for localizing sounds based on audio and visual cues. This model will combine prior information about where sounds generally originate with sensory information about the likelihood that a specific sound came from a particular location. The resulting posterior distribution not only allows us to make optimal decision about the sound's origin, but also lets us quantify how uncertain that decision is. Bayesian techniques are therefore useful normative models: the behavior of human or animal subjects can be compared against these models to determine how efficiently they make use of information.

Overview of this tutorial

1. Implement a Gaussian distribution
2. Use Bayes' Theorem to find the posterior from a Gaussian-distributed prior and likelihood.
3. Change the likelihood mean and variance and observe how posterior changes.
4. Advanced (optional): Observe what happens if the prior is a mixture of two gaussians?