Hardware for computing for non-ICT specialists

Difficulty level: Beginner

Duration: 43:21

Speaker: : Steve Furber

Computer arithmetic is necessarily performed using approximations to the real numbers they are intended to represent, and consequently it is possible for the discrepancies between the actual solution and the approximate solutions to diverge, i.e. to become increasingly different. This lecture focuses on how this happens and techniques for reducing the effects of these phenomena and discuss systems which are chaotic.

Difficulty level: Beginner

Duration: 36:56

Speaker: : David Lester

This lecture will addresses what it means for a problem to have a computable solution, methods for combining computability results to analyse more complicated problems, and finally look in detail at one particular problem which has no computable solution: the halting problem.

Difficulty level: Beginner

Duration: 28:28

Speaker: : David Lester

This lecture focuses on computational complexity which lies at the heart of computer science thinking. In short, it is a way to quickly gauge an approximation to the computational resource required to perform a task. Methods to analyse a computer program and to perform the approximation are presented. Speaker: David Lester.

Difficulty level: Beginner

Duration: 27:33

Speaker: : David Lester

Course:

This lecture focuses on where and how Jupyter notebooks can be used most effectively for education

Difficulty level: Beginner

Duration: 34:53

Speaker: : Thomas Kluyver.

Course:

JupyterHub is a simple, highly extensible, multi-user system for managing per-user Jupyter Notebook servers, designed for research groups or classes. This lecture covers deploying JupyterHub on a single server, as well as deploying with Docker using GitHub for authentication.

Difficulty level: Beginner

Duration: 1:36:27

Speaker: : Thomas Kluyver.

Course:

This tutorial is part 2 of 2. It aims to provide viewers with an understanding of the fundamentals of R tool.

Difficulty level: Beginner

Duration: 1:32:59

Speaker: : Edureka

The Virtual Brain is an open-source, multi-scale, multi-modal brain simulation platform. In this lesson, you get introduced to brain simulation in general and to The Virtual brain in particular. Prof. Ritter will present the newest approaches for clinical applications of The Virtual brain - that is, for stroke, epilepsy, brain tumors and Alzheimer’s disease - and show how brain simulation can improve diagnostics, therapy and understanding of neurological disease.

Difficulty level: Beginner

Duration: 1:35:08

Speaker: : Petra Ritter

The concept of neural masses, an application of mean field theory, is introduced as a possible surrogate for electrophysiological signals in brain simulation. The mathematics of neural mass models and their integration to a coupled network are explained. Bifurcation analysis is presented as an important technique in the understanding of non-linear systems and as a fundamental method in the design of brain simulations. Finally, the application of the described mathematics is demonstrated in the exploration of brain stimulation regimes.

Difficulty level: Beginner

Duration: 1:49:24

Speaker: : Andreas Spiegler

The simulation of the virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved results in clinics. The fundamentals of epilepsy are explained. On this basis, the concept of epilepsy simulation is developed. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.

Difficulty level: Beginner

Duration: 1:28:53

Speaker: : Julie Courtiol

The practical usage of The Virtual brain in its graphical user interface and via python scripts is introduced. In the graphical user interface, you are guided through its data repository, simulator, phase plane exploration tool, connectivity editor, stimulus generator and the provided analyses. The implemented iPython notebooks of TVB are presented, and since they are public, can be used for further exploration of The Virtual brain.

Difficulty level: Beginner

Duration: 1:12:24

Speaker: : Paul Triebkorn

Course:

A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner

Duration: 1:16:36

Speaker: : Tal Yarkoni

Course:

Tutorial on collaborating with Git and GitHub. This tutorial was part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Intermediate

Duration: 2:15:50

Speaker: : Elizabeth DuPre

Course:

The probability of a hypothesis, given data.

Difficulty level: Beginner

Duration: 7:57

Speaker: : Barton Poulson

Course:

Why math is useful in data science.

Difficulty level: Beginner

Duration: 1:35

Speaker: : Barton Poulson

Course:

Why statistics are useful for data science.

Difficulty level: Beginner

Duration: 4:01

Speaker: : Barton Poulson

Course:

Statistics is exploring data.

Difficulty level: Beginner

Duration: 2:23

Speaker: : Barton Poulson

Course:

Graphical data exploration

Difficulty level: Beginner

Duration: 8:01

Speaker: : Barton Poulson

Course:

Numerical data exploration

Difficulty level: Beginner

Duration: 5:05

Speaker: : Barton Poulson

Course:

Simple description of statistical data.

Difficulty level: Beginner

Duration: 10:16

Speaker: : Barton Poulson

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