Information Retrieval System
 

This subtitle uses the keyword "Information Retrieval" and highlights four core areas covered in your course: Search Al
What you'll learn
Comprehend and apply the basic concepts of information retrieval.
Applying searching procedure for user-text, designs and implement the system
Explore the skills in problem solving using systematic approaches
Analyze the limitations of different information retrieval techniques
Requirements
Basic Programming Skills Ability to write and understand simple code (preferably in Python). No advanced programming is required.
Description
This course provides a comprehensive introduction to Information Retrieval (IR) Systems, which are at the core of search engines, digital libraries, recommendation platforms, and many AI applications. Students will explore the techniques and algorithms that allow machines to process, index, and retrieve relevant information from large collections of unstructured data.Key topics include document representation, indexing, Boolean and vector space models, ranking algorithms, web search, evaluation metrics, relevance feedback, query expansion, and the role of natural language processing (NLP) in retrieval systems.Through hands-on exercises, case studies, and mini-projects, students will gain both theoretical knowledge and practical experience in building and evaluating IR systems.Learning Outcomes:Understand the architecture and components of modern IR systemsApply indexing and retrieval models to textual dataEvaluate IR performance using standard metrics like precision, recall, and MAPExplore advanced topics such as web crawling, link analysis, and personalized searchGain exposure to tools and techniques used in real-world IR applicationsThis course provides a comprehensive introduction to Information Retrieval (IR) Systems, which are at the core of search engines, digital libraries, recommendation platforms, and many AI applications. Students will explore the techniques and algorithms that allow machines to process, index, and retrieve relevant information from large collections of unstructured data.Key topics include document representation, indexing, Boolean and vector space models, ranking algorithms, web search, evaluation metrics, relevance feedback, query expansion, and the role of natural language processing (NLP) in retrieval systems.Through hands-on exercises, case studies, and mini-projects, students will gain both theoretical knowledge and practical experience in building and evaluating IR systems.Learning Outcomes:Understand the architecture and components of modern IR systemsApply indexing and retrieval models to textual dataEvaluate IR performance using standard metrics like precision, recall, and MAPExplore advanced topics such as web crawling, link analysis, and personalized searchGain exposure to tools and techniques used in real-world IR applications
Who this course is for
Have a foundational understanding of data structures, algorithms, and basic probability/statistics.
Are curious about how search engines, recommendation systems, and document retrieval work behind the scenes.
Want to explore the design and evaluation of systems that support efficient information access, including web search, semantic retrieval, and personalized recommendations.
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