Lesson type
Difficulty level
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.
Difficulty level: Beginner
Duration: 54:58
Speaker: : Franco Pestilli
Introduction of the Foundations of Machine Learning in Python course - Day 01.
High-Performance Computing and Analytics Lab, University of Bonn
Difficulty level: Beginner
Duration: 35:24
Speaker: : Elena Trunz
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
Difficulty level: Advanced
Duration: 34:52
Speaker: : Moritz Wolter
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
Difficulty level: Advanced
Duration: 57.45
Speaker: : Moritz Wolter
Support Vector Machines - Day 06 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Difficulty level: Advanced
Duration: 53.39
Speaker: : Elena Trunz
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
Difficulty level: Advanced
Duration: 1:15:39
Speaker: : Elena Trunz
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
Difficulty level: Advanced
Duration: 59:35
Speaker: : Elena Trunz
Dimensionality Reduction - Day 09 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Difficulty level: Advanced
Duration: 51:02
Speaker: : Elena Trunz
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
Difficulty level: Advanced
Duration: 54:12
Speaker: : Moritz Wolter
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
Difficulty level: Advanced
Duration: 42:07
Speaker: : Moritz Wolter
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
Difficulty level: Advanced
Duration: 42:07
Speaker: : Moritz Wolter
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
Difficulty level: Advanced
Duration: 16:45
Speaker: : Moritz Wolter
Sequence Processing - Day 15 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn
Difficulty level: Advanced
Duration: 47:45
Speaker: : Moritz Wolter
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.
Difficulty level: Beginner
Duration: 1:16:04
Speaker: : Erin Dickie and Sejal Patel
This lecture covers the benefits and difficulties involved when re-using open datasets, and how metadata is important to the process.
Difficulty level: Beginner
Duration: 11:20
Speaker: : Elizabeth DuPre
This lesson provides a quick tour of some data repositories and how to download and manipulate data from them.
Difficulty level: Beginner
Duration: 00:49:06
Speaker: : Sebastian Urchs
Course:
KnowledgeSpace (KS) is a data discoverability portal and neuroscience encyclopedia that was developed to make it easier for the neuroscience community to find publicly available datasets that adhere to the FAIR Principles and to provide an integrated view of neuroscience concepts found in Wikipedia and NeuroLex linked with PubMed and 17 of the world's leading neuroscience repositories. In short, KS provides a single point of entry where reseaerchers can search for a neuroscience concept of interest and receive results that include: i. a description of the term found in Wikipedia/NeuroLex, ii. links to publicly available datasets related to the concept of interest, and iii. up-to-date references that support the concept of interests found in PubMed. APIs are available so that developers of other neuroscience research infrastructures can integrate KS components in their infrastructures. If your repository or your favorite repository is not indexed in KS, please contact us.
Difficulty level: Beginner
Duration: 6:14
Speaker: : Heather Topple
This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.
This lesson corresponds to slides 1-64 in the PDF below.
Difficulty level: Intermediate
Duration: 1:28:14
Speaker: : Andreea Diaconescu
This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).
This lesson corresponds to slides 65-90 of the PDF below.
Difficulty level: Intermediate
Duration: 1:15:04
Speaker: : Daniel Hauke
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.
Difficulty level: Beginner
Duration: 2:24:35
Speaker: : Paul F.M.J. Verschure