CS 5782 - Introduction to Deep Learning

Spring. 4 credits. Student option grading (no audit).

Prerequisite: ECE 4200, STSCI 3740, CS 1110, CS 3780, and CS 2110. Co-meets with CS 4782.

K. Weinberger, J. Sun.

This class is an introductory course to deep learning. It covers the fundamental principles behind training and inference of deep networks, the specific architecture design choices applicable for different data modalities, discriminative and generative settings, and the ethical and societal implications of such models.

Outcome 1: Demonstrate the ability to perform neural network training and inference.

Outcome 2: Identify the correct neural network architecture choices for a given data modality.

Outcome 3: Implement a working deep learning pipeline for vision and language tasks.