Machine learning

Related collection

Neuromatch Academy 2020

Machine learning

By
Neuromatch Academy
Category
Computational neuroscience
Level
Beginner

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models. 

 

This course provides an overview of generalized linear models (GLM). 

Course Features
Lectures
Interactive Tutorials
Suggested reading
Recordings of question and answer sessions
Discussion Forum on Neurostars.org
Lessons of this Course
1
1
Machine Learning I (Intro Lecture)
Duration:
33:58
Speaker:
Cristina Savin

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.

2
2
Tutorial 1: Generalized Linear Models (GLMs)
Duration:
8:09
Speaker:
Anqi Wu

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.

3
3
Tutorial 2: Classifiers and regularizers
Duration:
6:42
Speaker:
Anqi Wu

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
4
4
Machine Learning II (Outro Lecture)
Duration:
29:30
Speaker:
I. Memming Park

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 further develops the concepts introduced in Machine Learning I.

Recent courses