DATAC182
Download as PDF
DATA C182 - Designing, Visualizing and Understanding Deep Neural Networks
Subject
DATA
Course Number
C182
Department
Course Level
Undergraduate
Formerly Known As
Computer Science 182
Course Title
Designing, Visualizing and Understanding Deep Neural Networks
Course 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.
practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground.
Minimum
4
Maximum
4
Grading Basis
Default Letter Grade; P/NP Option
Method of Assessment
Alternative Final Assessment
Instructors
Gonzalez
Prerequisites
MATH 53, MATH 54, and COMPSCI 61B; COMPSCI 70 or STAT 134; COMPSCI 189 is recommended.
Repeat Rules
Course is not repeatable for credit.
Credit Restriction Courses. Students will receive no credit for this course if following the course(s) have already been completed.
-
Credit Restrictions.
Students will receive no credit for COMPSCI 182 after completing COMPSCI W182, or COMPSCI L182.
Credit Replacement Courses. Upon passing, students can use the following course(s) to replace a deficient grade for this course.
-
Student Learning Outcomes
Cross-Listed Course(s)
Formats
Discussion, Lecture
Term
Fall and Spring
Duration (in weeks)
15
Minimum Hours
3
Maximum Hours
3
Lecture Mode of Instruction
In Person, Online
Minimum Hours
1
Maximum Hours
1
Discussion Mode of Instruction
In Person, Online
Minimum Hours
8
Maximum Hours
8