This lesson gives a primer to project management in a scientific context, with a particular neuroinformatic case study. 

In this lesson, you will hear about the current challenges regarding data management, as well as policies and resources aimed to address them. 

This lesson provides an overview of how to manage relationships in a research context, while highlighting the need for effective communication at various levels.

In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works. 

This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health. 

This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.

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 provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

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

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

Introduction to Convolutional Neural Networks  -  Day 11 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Initialization, Optimization, and Regularization  -  Day 12 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

U-Nets for medical Image-Segmentation  -  Day 13 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn