Introduction to Deep Learning

Catalog description

Instructional Method: Lecture/Lab Prerequisites: 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. Students will cover deep reinforcement learning algorithms including deep Q-learning and policy gradient methods. Deep unsupervised models such as auto-encoders, and deep generative models including variational auto-encoders and generative adversarial networks. Encoder-decoder architectures and their applications in real-world problems such as machine translation, image captioning, visual question answering, and text summarization.