This course offers an introduction to neural networks and deep learning. Topics include perceptrons, single-layer neural networks, multi-layer neural networks, Tensorflow and Keras, convolutional neural networks, transfer learning, deep learning methods for object recognition and object detection in images, and sequential learning models for analyzing text. Auto-encoders and generative adversarial networks will be covered to some extent. A strong programming and algorithmic background is assumed, as well as familiarity with linear algebra (vector and matrix operations). Prerequisite: Admitted into an Engineering Professional Program. C or better in CSE 3380 or MATH 3330, and C or better in IE 3301 or MATH 3313.