Course:

This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

Difficulty level: Intermediate

Duration: 1:09:33

Speaker: : Sean Hill

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

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 briefly goes over the outline of the Neuroscience for Machine Learners course.

Difficulty level: Intermediate

Duration: 3:05

Speaker: : Dan Goodman

This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models.

Difficulty level: Intermediate

Duration: 6:33

Speaker: : Marcus Ghosh

In this lesson you will learn how machine learners and neuroscientists construct abstract computational models based on various neurophysiological signalling properties.

Difficulty level: Intermediate

Duration: 10:52

Speaker: : Dan Goodman

Whereas the previous two lessons described the biophysical and signalling properties of individual neurons, this lesson describes properties of those units when part of larger networks.

Difficulty level: Intermediate

Duration: 6:00

Speaker: : Marcus Ghosh

This lesson goes over some examples of how machine learners and computational neuroscientists go about designing and building neural network models inspired by biological brain systems.

Difficulty level: Intermediate

Duration: 12:52

Speaker: : Dan Goodman

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved.

Difficulty level: Intermediate

Duration: 3:54

Speaker: : Dan Goodman

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network.

Difficulty level: Intermediate

Duration: 9:40

Speaker: : Dan Goodman

- Standards and Best Practices (1)
- Bayesian networks (1)
- (-) Machine learning (19)
- Brain-hardware interfaces (1)
- Clinical neuroscience (1)
- General neuroscience (8)
- (-) General neuroinformatics (11)
- (-) Computational neuroscience (6)
- Statistics (2)
- (-) Computer Science (2)
- Genomics (3)
- Data science (1)
- Open science (2)