Independent study project on a topic relevant to their academic program, supervised by a University of Maryland, College Park faculty member. Requires application and approval.

Prerequisites: ENAI600 and ENAI601 This is the equivalent of ENAI602 for Spring 2026 only).

This course aims at providing a foundational treatment of modern machine learning at the masters level, complemented with comprehensive projects that involve implementing the state-of-the-art machine learning algorithms on several datasets. The course topics are divided into 3 categories: 1) Classics of learning, including Bayesian decision theory, maximum likelihood estimation, Fisher's discriminant analysis, nearest neighbor classification, and support vector machines, clustering and dimensionality reduction, 2) Modern approaches to learning, including neural networks, deep learning networks, generative adversarial networks, autoencoders, and transformers, 3) Engineering perspectives on learning, including the effects of quantization and hardware implementation challenges, introduction to neuromorphic hardware and learning, and introduction to secure machine learning.

This is the equivalent of ENAI606 for Spring 2026 only)

This course introduces graduate engineering students to the foundations and frontiers of Large Language Models (LLMs), with an emphasis on both conceptual understanding and practical skills. We begin with core topics in natural language processing, tokenization, and language representation, then explore the transformer architecture, attention mechanisms, and the full LLM pipeline-from training to deployment. Students will examine key developments such as in-context learning, emergent capabilities, and prompt engineering, as well as multimodal extensions like Vision-Language Models (VLMs). Later weeks will address ethical considerations and recent trends in unified multimodal models. Through a mix of lectures, discussions, and hands-on assignments, students will be well-prepared to engage with cutting-edge research and real-world applications in generative AI.

This course delves into Quality Management Systems and Lean Six Sigma, offering a comprehensive understanding of quality principles, CMMI QMS, and Six Sigma DMAIC methodology. Participants will learn to develop Six Sigma projects, perform statistical analysis, create control charts, and conduct root cause analysis. Additionally, students will master the use of statistical distributions and hypothesis testing for quality improvement, as well as regression analysis and acceptance testing. By the course's end, attendees will be well-equipped to drive quality enhancements and process optimization within their organizations.