95-820 Applications of NL(X) and LLM
6 units, Carnegie Mellon University
The rapid adoption of Generative AI technologies is reshaping industries across the globe, from healthcare to financial services. According to Bain & Company, 87% of companies were already developing, piloting, or deploying generative AI by the start of 2024. Moreover, McKinsey's research estimates that generative AI could contribute an additional $2.6 trillion to $4.4 trillion annually across various sectors. These figures underscore the urgent need for professionals equipped with specialized knowledge in Natural Language Processing and Understanding (NL(X)) and Large Language Models (LLMs). This course is designed to provide graduate-level students with a comprehensive understanding of NL(X) and LLMs, focusing on their applications, evaluation, and operationalization across diverse industries. Beginning with the fundamentals of NL(X), the course covers its history, evolution, and critical applications, offering students hands-on experience with essential tools such as text mining, sentiment analysis, and embeddings. As students' progress, they will delve into advanced architectures, including RNNs, LSTMs, and Transformers, learning how these models drive key applications like machine translation and named entity recognition (NER). The course places significant emphasis on Large Language Models, such as GPT and BERT, guiding students through the intricacies of training, fine-tuning, and deploying these models. Advanced topics like Retrieval-Augmented Generation (RAG) and agentic architectures will also be explored, highlighting how LLM-based agents are transforming tasks that require complex reasoning, planning, and execution. To bridge the gap between theory and practice, the course offers detailed instruction on LLMOps, covering best practices for transitioning models from development to production. For more information visit: https://api.heinz.cmu.edu/courses_api/course_detail/95-820/
Prerequisites: 90-803 (min C) or 95-828 (min C) or 11-685 (min C) or 17-644 (min C) or 11-785 (min C) or 10-601 (min C)
Offered: Fall 2026, Fall 2025
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