Deep Learning for Computer Vision 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.