COMPSCI C182
Designing, Visualizing and Understanding Deep Neural Networks
Deep Networks have revolutionized computer vision, language technology, robotics and control. They have growing impact in many other areas of science and engineering. They do not however, follow a closed or compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground.
Topics
- Students will learn design principles and best practices: design motifs that work well in particular domains, structure optimization and parameter optimization. Students will come to understand visualizing deep networks. Exploring the training and use of deep networks with visualization tools. Understanding deep networks. Methods with formal guarantees: generative and adversarial models, tensor factorization.
Official catalogue course objectives and student learning outcomes
Sources
- Course catalogue ↗2026-2027