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CS 487

Fundamentals of Deep Learning

Catalog2026-2027
Credits3 units
LevelUpper Division
Average gradeB
Standard

Overview

Description

Principles, design and implementation of deep learning systems. Topics include statistical machine learning, multi-layer perceptron (MLP) and neural networks, deep neural networks, optimization and learning, convolutional neural networks (CNN), CNN architectures, CNN applications in classification, detection, segmentation, and advanced topics in recurrent networks and generative adversarial networks (GAN).

Requirements

Prerequisites

  • CS 302 with “C” or better
CS 302MATH 330

Recommended Preparation

  • Machine Learning
  • solid mathematical background
  • good programming skills
Original catalog text

Prerequisites

Prerequisite(s): CS 302 with “C” or better; MATH 330.

Recommended Preparation

Recommended Preparation: Machine Learning, solid mathematical background and good programming skills.

Units

Lecture3

Catalog Details

Offering

Offered: Every Spring

Terms

spring

Attributes

Standard

Learning Outcomes

  • identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
  • develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.