CS646: Information Retrieval

COMPSCI 646 is a graduate-level course in Information Retrieval, the science and engineering of organizing, searching, and making sense of large amounts of mostly unstructured (typically textual) data. The impact of IR research is most visible from the Web search engines, where many people actively interact with IR systems, but IR research goes beyond Web search. The class focuses primarily on the theory, design, and implementation of effective models for information organization and retrieval. The course also covers current research problems and methodologies in the field of IR.

Content

Prerequisites

• Proficiency in Python (using PyTorch)

  Programming assignments will be in Python.

• Basic Probability and Statistics
• Foundations of Machine Learning

Textbooks

• [SE]: W. Bruce Croft, Donald Metzler, and Trevor Strohman. Search Engines: Information Retrieval in Practice.
• [IIR]: Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Retrieval.

Resources to get familiar with neural networks:

• Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning
• Michael A. Nielsen. Neural Networks and Deep Learning
• Eugene Charniak. Introduction to Deep Learning

Syllabus