In this lecture, we will take a look at neural networks. First, we will see what neural networks are from the structural point of view and as a hypothesis class. Then we will look at the problem of training the parameters of a neural network using backpropagation.
Note that neural networks represent a vast collection of topics and the lecture only covers a very small slice. Students are encouraged to read the references if they are interested in learning more about this topic.
Lecture Slides
Links and Resources

Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville: An online book that focuses on deep neural networks. Part II of the book goes into depth about deep neural networks.

Deep Learning in Neural Networks: An Overview by Juergen Schmidhuber

Chapter 20 of Understanding Machine Learning: From Theory to Algorithms by Shai ShalevShwartz and Shai BenDavid (available online)

A Primer on Neural Network Models for Natural Language Processing by Yoav Goldberg