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

Course:

This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.

Difficulty level: Intermediate

Duration: 5:17

Speaker: : Mike X. Cohen

Course:

This lesson instructs users on how to import electrophysiological neural data into MATLAB, as well as how to convert spikes to a data matrix.

Difficulty level: Intermediate

Duration: 11:37

Speaker: : Mike X. Cohen

Course:

In this lesson, users will learn how to appropriately sort and bin neural spikes, allowing for the generation of a common and powerful visualization tool in neuroscience, the histogram.

Difficulty level: Intermediate

Duration: 5:31

Speaker: : Mike X. Cohen

Course:

Followers of this lesson will learn how to compute, visualize and quantify the tuning curves of individual neurons.

Difficulty level: Intermediate

Duration: 13:48

Speaker: : Mike X. Cohen

Course:

This lesson demonstrates how to programmatically generate a spatial map of neuronal spike counts using MATLAB.

Difficulty level: Intermediate

Duration: 12:16

Speaker: : Mike X. Cohen

Course:

In this lesson, users are shown how to create a spatial map of neuronal orientation tuning.

Difficulty level: Intermediate

Duration: 13:11

Speaker: : Mike X. Cohen

This lecture discusses the the importance and need for data sharing in clinical neuroscience.

Difficulty level: Intermediate

Duration: 25:22

Speaker: : Thomas Berger

This lecture presents the Medical Informatic Platform's data federation for Traumatic Brain Injury.

Difficulty level: Intermediate

Duration: 25:55

Speaker: : Stefano Finazzi

This lecture gives insights into the Medical Informatics Platform's current and future data privacy model.

Difficulty level: Intermediate

Duration: 17:29

Speaker: : Yannis Ioannidis

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General neuroscience
(17)
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