SIS-724 Advanced Text Analytics with AI and Machine Learning (3)

This course teaches students to design, plan, execute, and report on a relatively large-scale AI-driven text analytics project. It includes multiple hands-on labs in both R and Python, and a semester-long research project. The textual data all around us, from social media, to websites, blogs, email archives, annual reports, meeting transcripts, newspapers, and much more, can fuel our analysis of companies, countries, organizational problems, or facilitate academic research for master’s theses or doctoral dissertations. Students explore the fundamentals of text analytics and data visualization beginning with statistical bag-of-words (BoW) approaches, moving to natural language processing (NLP) approaches including language models, word embeddings, text parsing, parts of speech (POS) tagging, and Named Entity Recognition (NER); then on to machine learning (ML) approaches, including unsupervised techniques such as topic modeling and clustering, and supervised techniques such as predictive modeling for regression and classification problems; ending with Deep Learning (DL), transformers, and large language models (LLMs) like GPT, BERT, and LLaMA that underlie Generative AI tools. Crosslist: ITEC-724. Grading: A-F only.