CS 482

CS 482: Deep Learning, Transformers, and Large Language Models

Prerequisites: CS 220 and either MATH 440, or (MATH 260 and [STAT 200 or STAT 301])

Credit Hours: (3)

This course introduces the foundational principles of deep learning and modern large-scale AI systems. The course covers learning as optimization, neural networks, convolutional and recurrent architectures, attention mechanisms, Transformers, and large language models (LLMs). Students examine how deep learning models are trained, evaluated, and deployed, with emphasis on model behavior, inference-time control, and real-world AI system considerations. The course balances conceptual understanding with practical exposure to modern deep learning workflows.

 

Detailed Description of Content of Course

Neural Networks

• Linear
• Convolutional
• Recurrent
• Modern
• Adversarial
• Multilayer Perceptrons

Attention Mechanisms and Transformers

Optimization

Computer Vision

Natural Language Processing

Reinforcement learning

 

Detailed Description of Conduct of Course

Primarily a lecture based class.  Programming projects are assigned to give students experience in implementing existing algorithms. Presentations and group work may be used.

 

Student Learning Outcomes

Students will be able to:

• Explain core principles of machine learning and deep learning
• Understand learning as an optimization process
• Design and train neural networks for vision and sequence-based tasks
• Explain attention mechanisms and Transformer architectures
• Analyze how large language models generate and control outputs
• Understand practical challenges in deploying and serving LLMs

 

Assessment Measures

Student achievement is measured by written tests and evaluation of homework and programming assignments.  Quizzes and presentations may be used.

 

Other Course Information

None.

 

Review and Approval

February 24, 2026