CS C182. Designing, Visualizing and Understanding Deep Neural Networks
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.
Units: 4
Also Offered As: DATA C182
Student Learning Outcomes: Students will learn design principles and best practices: design motifs that work well in particular domains, structure optimization and parameter optimization., Understanding deep networks. Methods with formal guarantees: generative and adversarial models, tensor factorization., Students will come to understand visualizing deep networks. Exploring the training and use of deep networks with visualization tools.
Prerequisites: MATH 53, MATH 54, and COMPSCI 61B; COMPSCI 70 or STAT 134; COMPSCI 189 is recommended.
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 L182, COMPSCI W182, COMPSCI W182, or COMPSCI L182.
Formats:
Fall: 3.0 hours of lecture and 1.0 hours of discussion per week
Spring: 3.0 hours of lecture and 1.0 hours of discussion per week
Grading Basis: letter
Final Exam Status: Alternative method of final assessment
Class Schedule (Spring 2025):
CS C182/282A – MoWe 14:00-15:29, Soda 306 –
Anant Sahai
Class Notes
* Time conflicts ARE allowed for this class but NO alternate final exam will be offered.
* Lecture WILL be recorded for playback later.
* NO alternate final exam will be offered.
* ONLY declared EECS/CS majors can waitlist or enroll - no DS majors will be accepted during SP25
Class Schedule (Fall 2025):
CS C182/282A – TuTh 11:00-12:29, Soda 306 –
Anant Sahai, Gireeja Vishnu Ranade
Class Notes
* Until the first day of class, all seats are allocated to declared EECS/CS majors with terms in attendance of 8 or more.
* No DS majors are allowed to take CS C182. They will be able to take DATA C182 in the future.
Links: