Search Results

This course focuses on the foundational principles of Information Retrieval (IR), covering Boolean search, vector space models, probabilistic retrieval, and evaluation metrics. It also explores modern advancements, such as dense vector representations and neural models like BERT and GPT, which enhance traditional IR techniques. Students will study text preprocessing, clustering, relevance feedback, and search engine design, with hands-on projects applying both classical methods and AI-driven tools. Emerging topics, including retrieval-augmented generation (RAG), explainable AI, and ethical considerations, prepare students to build and evaluate cutting-edge retrieval systems for diverse applications.

College/Department: College of Computing and Informatics
Repeat Status: Not repeatable for credit
Prerequisites: INFO 540 [Min Grade: C], DSCI 511 [Min Grade: C], DSCI 521 [Min Grade: C] (Can be taken Concurrently) or INFO 590 [Min Grade: C] or CS 502 [Min Grade: C]