Spring 2022
COMPSCI 182 107 - DIS 107
Formerly COMPSCI C182
Designing, Visualizing and Understanding Deep Neural Networks
Class #:30834
Units: 4
Instruction Mode:
In-Person Instruction
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
35
Enrolled: 0
Waitlisted: 0
Capacity: 35
Waitlist Max: 0
No Reserved Seats
Hours & Workload
1 hours of the exchange of opinions or questions on course material per week, 3 hours of instructor presentation of course materials per week, and 8 hours of outside work hours per week.
Course Catalog Description
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.
Rules & Requirements
Credit Restrictions
Students will receive no credit for COMPSCI 182 after completing COMPSCI W182, or COMPSCI L182. A deficient grade in COMPSCI 182 may be removed by taking COMPSCI W182, or COMPSCI L182.
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
No Reserved Seats
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials