CSC 5611 - Deep Learning

4 lecture hours 0 lab hours 4 credits

Course Description
This course provides an in-depth introduction to the foundations of deep learning. Students will learn how to architect, train, and evaluate deep neural networks. Students will gain experience with backpropagation, a variety of network structures, and a variety of options for training networks.
Prereq: CSC 4601 or CSC 5601 or CSC 6621 or instructor consent
Note: This course is open to qualified undergraduate students.

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Understand the process of backpropagation and its role in training deep networks
Implement a basic deep learning network library
Apply deep learning to a unique dataset and analyze performance
Compare options for training deep neural networks, for example, optimization methods, generalization methods, normalization, initialization methods
Apply transfer learning to leverage pre-trained models
Compare various modern network architectures, for example, convolutional neural networks (CNNs), encoder-decoders, generative adversarial networks (GANs), or transformers. Emphasis on topics can vary depending on the instructor's specialties
Evaluate training methods and alternative architectures based on model accuracy, constraints, and training performance
Discuss the ethical implications of deep neural network applications
Compare the performance of the from-scratch deep neural network library implementation to an existing library when applied to a unique dataset

Prerequisites by Topic

Machine learning
Python fundamentals

Coordinator
Dr. Josiah Yoder

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