Natural Language

Catalog description

This course provides a doctoral-level investigation into Natural Language Processing (NLP), the interdisciplinary field combining linguistics, computer science, and artificial intelligence. Students will transition from traditional rule-based and statistical methods to the modern era of Large Language Models (LLMs) and generative text. The course focuses on the challenges of teaching machines to understand the nuances of human communication, including syntax, semantics, and pragmatics. A significant portion of the course is dedicated to the Transformer architecture, exploring self-attention mechanisms, pre-training objectives, and the fine-tuning of foundation models. Students will evaluate the technical and ethical complexities of deploying language technologies, such as machine translation, sentiment analysis, and conversational agents, within real-world enterprise environments. AIT 816 Advanced Topics in Natural Language Processing and Innovation (3 Credits) This course investigates cutting-edge developments in Natural Language Processing (NLP), including deep learning techniques for sentiment analysis, question answering, text classification, and conversational AI. Students will explore transformer models, embedding techniques, and domain-specific adaptation of NLP systems. The course emphasizes innovation through applied research and development of NLP applications that solve real-world challenges in business, healthcare, and digital services. AIT 820 Generative AI and Language Models (3 Credits) This course explores the foundations and applications of generative artificial intelligence, with a particular focus on large language models (LLMs) such as GPT, BERT, and their successors. Students will examine the architecture, training methods, and fine-tuning of generative models, as well as their practical use cases in content generation, chatbots, summarization, and coding assistants. Ethical considerations, bias mitigation, and responsible AI use are emphasized, preparing students to evaluate and apply generative AI in complex technical and organizational contexts. AIT 825 Applied AI and Machine Learning (3 Credits) This course provides hands-on experience with applying artificial intelligence and machine learning techniques to solve complex, real-world problems. Students will design, develop, and evaluate AI systems using supervised and unsupervised learning, reinforcement learning, and deep learning techniques. Emphasis is placed on model deployment, evaluation metrics, and alignment with business or societal objectives. The course prepares students to lead AI initiatives from proof of concept to production in dynamic organizational environments.