This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology. 

This lecture provides an introductory overview of some of the most important concepts in software engineering.

This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs. 

This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects. 

In this lesson, you will learn how to understand data management plans and why data sharing is important. 

This lecture gives a tour of what neuroethics is and how it applies to neuroscience and neurotechnology, while also addressing justice concerns within both fields. 

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.

This lesson gives an in-depth description of scientific workflows, from study inception and planning to dissemination of results. 

This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.

In this final lecture of the INCF Short Course: Introduction to Neuroinformatics, you will hear about new advances in the application of machine learning methods to clinical neuroscience data. In particular, this talk discusses the performance of SynthSeg, an image segmentation tool for automated analysis of highly heterogeneous brain MRI clinical scans.

Computer arithmetic is necessarily performed using approximations to the real numbers they are intended to represent, and consequently it is possible for the discrepancies between the actual solution and the approximate solutions to diverge, i.e. to become increasingly different. This lecture focuses on how this happens and techniques for reducing the effects of these phenomena and discuss systems which are chaotic.

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

High-Performance Computing and Analytics Lab, University of Bonn

Optimization for machine learning - Day 02 lecture of the Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Linear Algebra for Machine Learning - Day 03 lecture of the Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Support Vector Machines -  Day 06 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

This lecture will addresses what it means for a problem to have a computable solution, methods for combining computability results to analyse more complicated problems, and finally look in detail at one particular problem which has no computable solution: the halting problem.

Decision Trees and Random Forests -  Day 07 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Clustering and Density Estimation -  Day 08 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Dimensionality Reduction -  Day 09 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Introduction to Neural Networks -  Day 10 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn