CS 498 DL4 - Intro to Deep Learning

Spring 2021

Intro to Deep LearningCS498DL347231E1331100 - 1215 M W    
Intro to Deep LearningCS498DL440484E1341100 - 1215 M W    Svetlana Lazebnik

Official Description

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.

Section Description

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.