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 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.

Difficulty level: Advanced

Duration: 50:28

Speaker: : Pierre Bellec

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

Course:

In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity.

Difficulty level: Intermediate

Duration: 1:16:10

Speaker: : John Griffiths

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 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

This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course.

Difficulty level: Intermediate

Duration: 5:58

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

- (-) Standards and Best Practices (1)
- Bayesian networks (1)
- (-) Machine learning (23)
- Animal models (1)
- Brain-hardware interfaces (1)
- Clinical neuroscience (1)
- General neuroscience (9)
- (-) General neuroinformatics (11)
- (-) Computational neuroscience (11)
- Statistics (2)
- Computer Science (3)
- Genomics (3)
- Data science (2)
- Open science (2)