Introduction of the Foundations of Machine Learning in Python course - Day 01.
High-Performance Computing and Analytics Lab, University of Bonn
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases.
This lecture provides an overview of some of the essential concepts in neuropharmacology (e.g. receptor binding, agonism, antagonism), an introduction to pharmacodynamics and pharmacokinetics, and an overview of the drug discovery process relative to diseases of the central nervous system.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
This demonstration walks through how to import your data into MATLAB.
This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses.
This tutorial outlines, step by step, how to perform analysis by group and how to do change-point detection.
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 lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
This lecture covers the description and brief history of data science and its use in neuroinformatics.
This lesson provides an overview of self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.
This lesson gives a quick walkthrough the Tidyverse, an "opinionated" collection of R packages designed for data science, including the use of readr, dplyr, tidyr, and ggplot2.
This lesson provides a hands-on tutorial for generating simulated brain data within the EBRAINS ecosystem.