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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