Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were 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.

This lecture covers advanced concepts of energy based models. The lecture is a part of the Advanced energy based models modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models I, Energy based models II, and Introduction to Data Science or a Graduate Level Machine Learning.

This lecture covers advanced concepts of energy based models. The lecture is a part of the Advanced energy based models modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models I, Energy based models II, Energy based models III, and Introduction to Data Science or a Graduate Level Machine Learning.

This tutorial covers LV-EBM to target prop to (vanilla, denoising, contractive, variational) autoencoder and a part of the Advanced energy based models modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models I, Energy based models II, Energy based models III, Energy based models IV, and Introduction to Data Science or a Graduate Level Machine Learning.

This lecture covers advanced concepts of energy based models. The lecture is a part of the Advanced energy based models modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models I, Energy based models II, Energy based models III, Energy based models IV, and Introduction to Data Science or a Graduate Level Machine Learning.

This tutorial covers the concepts of autoencoders, denoising encoders, and variational autoencoders (VAE) with PyTorch, as well as generative adversarial networks and code. It is a part of the Advanced energy based models modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models I, Energy based models II, Energy based models III, Energy based models IV, Energy based models V, and Introduction to Data Science or a Graduate Level Machine Learning.

This lecture covers advanced concepts of energy based models. The lecture is a part of the Associative memories modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models I, Energy based models II, Energy based models III, Energy based models IV, Energy based models V, and Introduction to Data Science or a Graduate Level Machine Learning.

This tutorial covers advanced concept of energy based models. The lecture is a part of the Associative memories modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. 

This lecture provides an introduction to the problem of speech recognition using neural models, emphasizing the CTC loss for training and inference when input and output sequences are of different lengths. It also covers the concept of beam search for use during inference, and how that procedure may be modeled at training time using a Graph Transformer Network. It is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Modules 1 - 5 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

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 CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Modules 1 - 5 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

This tutuorial covers the concept of Graph convolutional networks and is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Modules 1 - 5 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

This lecture covers the concept of model predictive control and is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Models 1-6 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

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 CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Models 1-6 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

This lecture covers the concept of predictive policy learning under uncertainty and is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Models 1-6 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

This lecture covers the concepts of gradient descent, stochastic gradient descent, and momentum. It is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Models 1-7 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

This lecture covers the concepts of gradient descent, stochastic gradient descent, and momentum. It is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Models 1-7 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

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. 

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. 

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.