Deep Learning, Transformers, and Large Language Models

Catalog description

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