CS 5782

Introduction to Deep Learning

Course Description

Course information provided by the 2026-2027 Catalog.

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.

Prerequisites background equivalent to: ECE 4200; STSCI 3740; CS 1110; CS 3780; CS 2110.

Enrollment Information Enrollment limited to: Computer Science (CS) Master of Engineering (M.Eng.) students.

Last 4 Terms Offered 2026SP, 2025SP

Learning Outcomes

• Demonstrate the ability to perform neural network training and inference.
• Identify the correct neural network architecture choices for a given data modality.
• Implement a working deep learning pipeline for vision and language tasks.
• Analyze deep learning papers critically and synthesize paper summary, strengths, and weaknesses into the form of a quiz

View Enrollment Information