Information Retrieval Systems

Information retrieval is the problem of finding relevant content in unstructured text collections — the machinery behind search engines, recommender feeds, and filtering systems — and this course works through how those systems actually represent documents, match queries, and get evaluated when there's no clean schema like a database has. You'll build up the toolkit piece by piece: inverted and signature files, term weighting and stemming, clustering and cluster-based retrieval, then push into web search, information filtering, and newer problems like stance detection on streaming data. Assessment is light on exams (one midterm, one final) and heavy on a term project where you implement and evaluate something real. It pairs naturally with data structures and algorithms background, and sets you up for NLP, data mining, and any work touching large-scale text.