Information Retrieval: Implementing and Evaluating Search Engines (The MIT Press)

An introduction to information retrieval, the foundation for modern search engines, that emphasizes implementation and experimentation.

Information retrieval is the foundation for modern search engines. This textbook offers an introduction to the core topics underlying modern search technologies, including algorithms, data structures, indexing, retrieval, and evaluation. The emphasis is on implementation and experimentation; each chapter includes exercises and suggestions for student projects. Wumpus―a multiuser open-source information retrieval system developed by one of the authors and available online―provides model implementations and a basis for student work. The modular structure of the book allows instructors to use it in a variety of graduate-level courses, including courses taught from a database systems perspective, traditional information retrieval courses with a focus on IR theory, and courses covering the basics of Web retrieval. In addition to its classroom use, Information Retrieval will be a valuable reference for professionals in computer science, computer engineering, and software engineering.

• Introduction to Information Retrieval
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• Relevant Search: With applications for Solr and Elasticsearch
• Deep Learning with Python
• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
• Deep Learning (Adaptive Computation and Machine Learning)
• Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
• Practical Statistics for Data Scientists: 50 Essential Concepts
• Recommender Systems: The Textbook