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Nov 21, 2024
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CSC 5611 - Deep Learning4 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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