Course:

This lecture covers the concepts of the architecture and convolution of traditional convolutional neural networks, the characteristics of graph and graph convolution, and spectral graph convolutional neural networks and how to perform spectral convolution, as well as the complete spectrum of Graph Convolutional Networks (GCNs), starting with the implementation of Spectral Convolution through Spectral Networks. It then provides insights on applicability of the other convolutional definition of Template Matching to graphs, leading to Spatial networks. This lecture 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: 2:00:22

Speaker: : Xavier Bresson

Course:

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

Course:

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

Course:

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

Course:

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

Course:

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

Course:

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

Course:

In this lesson, users will learn about human brain signals as measured by electroencephalography (EEG), as well as associated neural signatures such as steady state visually evoked potentials (SSVEPs) and alpha oscillations.

Difficulty level: Intermediate

Duration: 8:51

Speaker: : Mike X. Cohen

Course:

This lecture describes the principles of EEG electrode placement in both 2- and 3-dimensional formats.

Difficulty level: Intermediate

Duration: 12:16

Speaker: : Mike X. Cohen

Course:

This tutorial walks users through performing Fourier Transform (FFT) spectral analysis of a single EEG channel using MATLAB.

Difficulty level: Intermediate

Duration: 13:39

Speaker: : Mike X. Cohen

Course:

This tutorial builds on the previous lesson's demonstration of spectral analysis of one EEG channel. Here, users will learn how to compute and visualize spectral power from all EEG channels using MATLAB.

Difficulty level: Intermediate

Duration: 12:34

Speaker: : Mike X. Cohen

Course:

In this lesson, users will learn more about the steady-state visually evoked potential (SSEVP), as well as how to create and interpret topographical maps derived from such studies.

Difficulty level: Intermediate

Duration: 9:10

Speaker: : Mike X. Cohen

Course:

This lesson teaches users how to extract edogenous brain waves from EEG data, specifically oscillations constrained to the 8-12 Hz frequency band, conventionally named alpha.

Difficulty level: Intermediate

Duration: 13:23

Speaker: : Mike X. Cohen

Course:

In the final lesson of this module, users will learn how to correlate endogenous alpha power with SSVEP amplitude from EEG data using MATLAB.

Difficulty level: Intermediate

Duration: 12:36

Speaker: : Mike X. Cohen

This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

Difficulty level: Intermediate

Duration: 1:11:04

Speaker: : Etay Hay

This is an in-depth guide on EEG signals and their interaction within brain microcircuits. Participants are also shown techniques and software for simulating, analyzing, and visualizing these signals.

Difficulty level: Intermediate

Duration: 1:30:41

Speaker: : Frank Mazza

Course:

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.

Difficulty level: Intermediate

Duration: 1:47:22

Speaker: : Erin Dickie and John Griffiths

Course:

This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices.

Difficulty level: Intermediate

Duration: 1:39:04

Speaker: : Erin Dickie and John Griffiths

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

- Bayesian networks (3)
- (-) Standards and Best Practices (1)
- (-) Machine learning (20)
- Animal models (1)
- Brain-hardware interfaces (1)
- Clinical neuroscience (1)
- General neuroscience (15)
- General neuroinformatics (11)
- Computational neuroscience (12)
- Statistics (5)
- (-) Computer Science (2)
- Genomics (8)
- Data science (2)
- Open science (4)