This course will cover the basics of modern deep neural networks. The first part of the course will introduce neural network architectures, activation functions, and operations. It will present different loss functions and describe how training is performed via backpropagation. In the second part, the course will describe specific types of neural networks, e.g. convolutional, recurrent, and graph networks, as well as their applications in computer vision and natural language processing. The course will also briefly discuss reinforcement learning and unsupervised learning, in the context of neural networks. In addition to attending lectures and completing bi-weekly homework assignments, students will also carry out and present a project.

Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad LG/SNC Basis
Course Requirements: PREQ: MATH 0230 and (MATH 0280 or MATH 1180) and CS 1501 with a minimum grade of C or TRANSFER.
Minimum Credits: 3
Maximum Credits: 3