CS 498 DL3 - Special Topics - Deep Learning
Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: 1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated in the same or separate terms if topics vary.
All class meetings will be online and synchronous. This course will provide an elementary hands-on introduction to neural networks and deep learning. Topics covered will include linear classifiers, multi-layer neural networks, back-propagation and stochastic gradient descent, convolutional neural networks, recurrent neural networks, generative networks, and deep reinforcement learning. Coursework will consist of programming assignments in TensorFlow or PyTorch. Those registered for 4 credit hours will have to complete a project. Prerequisites: multi-variable calculus, linear algebra, CS 361 or STAT 400. No previous exposure to machine learning is required.
Option 1Number of Required Visit(s): 0
Course Level: Graduate
Term(s): Fall , Spring