Skip to main content

This tutuorial covers the concept of graph convolutional networks and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Modules 1 - 5 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 57:33
Speaker: : Alfredo Canziani

This lecture covers the concept of model predictive control and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:10:22
Speaker: : Alfredo Canziani

This lecture covers the concepts of emulation of kinematics from observations and training a policy. It is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:01:21
Speaker: : Alfredo Canziani

This lecture covers the concept of predictive policy learning under uncertainty and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:14:44
Speaker: : Alfredo Canziani

This lecture covers the concepts of gradient descent, stochastic gradient descent, and momentum. It is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-7 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:29:05
Speaker: : Aaron DeFazio

This lecture continues on the topic of descent from the previous lesson, Optimization I. This lesson is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-7 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:51:32
Speaker: : Alfredo Canziani

This lesson gives an introduction to deep learning, with a perspective via inductive biases and emphasis on correctly matching deep learning to the right research questions.

Difficulty level: Beginner
Duration: 01:35:12
Speaker: : Blake Richards

The state of the field regarding the diagnosis and treatment of major depressive disorder (MDD) is discussed. Current challenges and opportunities facing the research and clinical communities are outlined, including appropriate quantitative and qualitative analyses of the heterogeneity of biological, social, and psychiatric factors which may contribute to MDD.

Difficulty level: Beginner
Duration: 1:29:28

This lesson delves into the opportunities and challenges of telepsychiatry. While novel digital approaches to clinical research and care have the potential to improve and accelerate patient outcomes, researchers and care providers must consider new population factors, such as digital disparity. 

Difficulty level: Beginner
Duration: 1:20:28
Speaker: : Abhi Pratap

This lesson provides a basic introduction to clinical presentation of schizophrenia, its etiology, and current treatment options.

Difficulty level: Beginner
Duration: 51:49

This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication? 

Difficulty level: Intermediate
Duration: 9:34
Speaker: : Dan Goodman

This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.

Difficulty level: Beginner
Duration: 8:11
Speaker: : Thomas Wachtler

This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.

Difficulty level: Beginner
Duration: 15:57
Speaker: : Andrew Davison

This lecture contains an overview of the Distributed Archives for Neurophysiology Data Integration (DANDI) archive, its ties to FAIR and open-source, integrations with other programs, and upcoming features.

Difficulty level: Beginner
Duration: 13:34

This lecture contains an overview of the Australian Electrophysiology Data Analytics Platform (AEDAPT), how it works, how to scale it, and how it fits into the FAIR ecosystem.

Difficulty level: Beginner
Duration: 18:56
Speaker: : Tom Johnstone

This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.

Difficulty level: Beginner
Duration: 15:51

This lecture will provide an overview of the INCF Training Suite, a collection of tools that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of TrainingSpace, Neurostars, and KnowledgeSpace.

Difficulty level: Beginner
Duration: 09:50
Speaker: : Mathew Abrams

This lecture contains an overview of the China-Cuba-Canada neuroinformatics ecosystem for Quantitative Tomographic EEG Analysis (qEEGt).

Difficulty level: Beginner
Duration: 12:56

This lesson describes the fundamentals of genomics, from central dogma to design and implementation of GWAS, to the computation, analysis, and interpretation of polygenic risk scores. 

Difficulty level: Intermediate
Duration: 1:28:16
Speaker: : Dan Felsky

This lesson contains the slides (pptx) of a lecture discussing the necessary concepts and tools for taking into account population stratification and admixture in the context of genome-wide association studies (GWAS). The free-access software Tractor and its advantages in GWAS are also discussed. 

Difficulty level: Intermediate
Duration:
Speaker: : Dan Felsky