How AI and ML Are Changing Information Retrieval - From Word Matching to Meaning Matching

Overview

Explore how AI transforms information retrieval systems, moving from word matching to meaning-based search, featuring OpenSearch capabilities, BM25 scoring, and vector-based approaches.

Syllabus

• Introduction to Information Retrieval
Overview of Traditional Information Retrieval
Limitations of Word Matching Approaches
• OpenSearch Capabilities in Modern IR Systems
Introduction to OpenSearch
Deploying Search at Scale
Hands-On Lab: Implementing an OpenSearch Instance
• BM25 Scoring and Word-Based Retrieval
Understanding the BM25 Algorithm
Improvements over Traditional Retrieval Methods
Practical Application and Fine-Tuning of BM25
• Transition from Word Matching to Meaning Matching
Introduction to Semantic Search
Limitations of Keyword-Based Search
• Vector-Based Approaches to Information Retrieval
Understanding Embeddings and Semantic Vectors
Comparison Between TF-IDF and Vector Space Models
Hands-On Lab: Implementing Vector-Based Search with Pre-Trained Models
• AI and ML Enhancements in Semantic Search
Role of Deep Learning and Transformers
Natural Language Processing (NLP) for Semantic Understanding
Case Study: BERT and Similar Models in Search
• Integrating AI in Information Retrieval Systems
Hybrid Techniques: Combining Classic and AI-Based Approaches
Evaluating Search Effectiveness: Precision, Recall, and F1 Score
Hands-On Lab: Building and Evaluating a Semantic Search System
• Future Trends in AI-Powered Information Retrieval
Emerging Technologies and Developments
Challenges and Ethical Considerations in AI-Driven Search
• Wrap-Up and Course Review
Key Takeaways
Resources for Further Learning
Q&A Session and Feedback Collection

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Subjects

Data Science