Aug 30, 2025  
2025-2026 Undergraduate Catalog 
    
Catalog Navigation
  Catalog Home

  Programs A-Z
  Programs by Type

  Academic Policies & Regulations
  Undergraduate Curriculum
  Course Descriptions

  Global Field Study
  Honors Program
  Interdisciplinary Programs & Courses
  Three-Year Degrees

  Accreditation
  Campus Map
  Disability Support Services
  Mission Statement
  Past Catalogs
  Student Handbook
  Academic Calendar
  Veteran’s Benefits
HELP
2025-2026 Undergraduate Catalog

CS 382 Deep Learning

4 credits
This course provides an introduction to Neural Networks, suitable for students with a background in Calculus I. We will explore essential linear algebra concepts relevant to neural networks, including matrices, vectors, and operations. Additionally, the course will cover the gradient method for optimization of loss functions. The latter half focuses on hands-on implementation in PyTorch, building and coding various neural network architectures. This includes simple linear perceptrons for regression problems, perceptrons with hidden layers and activation functions, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)

 

Spring

Prerequisite: MA 201