CS 5782
Introduction to Deep Learning
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.
Topics
- 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
Official roster learning outcomes
Prerequisites & course information
background equivalent to: ECE 4200; STSCI 3740; CS 1110; CS 3780; CS 2110.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Introduction to Deep Learning · 4.0 · Graduate