EECS 182
Designing, Visualizing and Understanding Deep Neural Networks, UC Berkeley, EECS, 2025
Description
Deep Networks have revolutionized computer vision, language technology, robotics and control. They have a growing impact in many other areas of science and engineering, and increasingly, on commerce and society. They do not however, fully follow any currently known 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 is a fancy way of saying “we don’t understand this stuff nearly well enough, but we have no choice but to muddle through anyway.” This course attempts to cover that ground and show students how to muddle through even as we aspire to do more. That said, we will be leveraging the substantial, though still tentative, understanding that we have gained in the past few years. It isn’t 2015 anymore… We know a lot more than we used to.
My Responsibilities
- Grading the final exam and final project.
- Composing homework and exam questions with their solutions.
- Teaching two discussion sections per week.