CS 444

Credit: 3 OR 4 hours.

Provides an elementary hands-on introduction to neural networks and deep learning with an emphasis on computer vision applications. Topics include: linear classifiers; multi-layer neural networks; back-propagation and stochastic gradient descent; convolutional neural networks and their applications to object detection and dense image labeling; recurrent neural networks and state-of-the-art sequence models like transformers; generative adversarial networks and variational autoencoders for image generation; and deep reinforcement learning. Coursework will consist of programming assignments in a common deep learning framework. Those registered for 4 credit hours will have to complete a project.

Same as ECE 494. 3 undergraduate hours. 4 graduate hours. Prerequisite: MATH 241; one of MATH 225, MATH 257, MATH 415, MATH 416, ASRM 406, or BIOE 210; CS 225; one of CS 361, STAT 361, ECE 313, MATH 362, MATH 461, MATH 463 or STAT 400. No previous exposure to machine learning is required.