CSE 543
Deep Learning
Foundations of modern deep learning and its applications. Topics include various forms of deep neural networks architectures such as convolution neural networks, recurrent networks, attention mechanism, training and optimization algorithms and their applications in domains such as computer vision, robotics, and natural language processing. Recommended: comfortable with programming; and working knowledge of linear algebra (MATH 208); vector calculus (MATH 126); probability and statistics (CSE 312 and STAT 390); algorithms (CSE 421); and machine learning (CSE 446 or CSE 546).
Topics
- various forms of deep neural networks architectures such as convolution neural networks
- recurrent networks
- attention mechanism
- training and optimization algorithms and their applications in domains such as computer vision
- robotics
- and natural language processing
Topics explicitly listed in official catalogue description
Sources
- Course catalogue ↗Current public catalogue; retrieved 2026-11-05