This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity. 

This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity. 

This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below. 

This lecture provides an introduction to Plato’s concept of rationality and Aristotle’s concept of empiricism, and the enduring discussion between rationalism and empiricism to this day.

This lecture goes into further detail about the hard problem of developing a scientific discipline for subjective consciousness.

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.

In this workshop talk, you will receive a tour of the Code Ocean ScienceOps Platform, a centralized cloud workspace for all teams. 

This talk describes approaches to maintaining integrated workflows and data management schema, taking advantage of the many open source, collaborative platforms already existing.

This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.

Introduction of the Foundations of Machine Learning in Python course - Day 01.

High-Performance Computing and Analytics Lab, University of Bonn

This lesson gives a brief introduction to the course Neuroscience for Machine Learners (Neuro4ML). 

This lesson covers the history of neuroscience and machine learning, and the story of how these two seemingly disparate fields are increasingly merging. 

In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience. 

This lesson gives an introduction to simple spiking neuron models.

This lesson provides a short reel on who we are, what we're doing and why we're doing it.

In this webinar, educators currently implementing collaborative annotation in their classrooms discuss their experiences with collaborative annotation and using Hythothes.is and Canvas App.

This tutorial provides an overview of how to use the feature of Hypothes.is.

This lesson gives a brief overview of the Hypothes.is functionality from an end user's perspective.

This video will teach you the basics of navigating the Open Science Framework and creating your first projects.