Basic Introduction to Deep Learning

This lecture includes a description of linear models and loss functions, gradient descent, single layer artificial neural networks, deep neural networks, and convolutional neural networks.

Foundations of Bayesian Learning

This lecture includes a description of probability fundamentals, binary hypothesis testing, parameter estimation, and identifying atypical brain subnetworks.

YouTube Lecture Part 1 | Part 2 | Part 3 | Slides

YouTube Lecture | Slides

'Dive into Deep Learning: An Interactive Deep Learning Book With Code, Math, and Discussions'

AI/ML Week 4 Data Exercise on Deep Learning

AI/ML Week 4 Deep Learning Solutions

AI/ML Week 4 Data Exercise on Bayesian ML

AI/ML Week 4 Bayesian ML Solutions