CSCI 381
Deep Learning Fall 2024
Division III Quantitative/Formal Reasoning

Class Details

This course is an introduction to deep neural networks and how to train them. Beginning with the fundamentals of regression and optimization, the course then surveys a variety of neural network architectures, which may include multilayer feedforward neural networks, convolutional neural networks, recurrent neural networks, and transformer networks. Students will also learn how to use deep learning software such as PyTorch or Tensorflow.
The Class: Format: lecture
Limit: 24
Expected: 24
Class#: 1056
Grading: no pass/fail option, no fifth course option
Requirements/Evaluation: Evaluation based on assignments, projects, and exams.
Prerequisites: CSCI 136 and fulfillment of the Discrete Mathematics Proficiency requirement
Enrollment Preferences: Current or expected Computer Science majors
Distributions: Division III Quantitative/Formal Reasoning
QFR Notes: The course will consist of programming assignments and problem sets in which quantitative/formal reasoning skills are practiced and evaluated.
Attributes: COGS Interdepartmental Electives

Class Grid

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