The objective is that participants in the course
- are familiar with the classic retrieval models
- understand the limitations and assumptions associated with these models
- have insight and proficiency in the design and construction of search engines
- are familiar with the standard evaluation methods for IR systems
- are familiar with interaction techniques to support searchers in their quest for information
- have an understanding of how the searcher's context and behaviour can be used to enhance retrieval effectiveness
- have learned how machine learnung and nueral network algorithms are utilized for information retrieval systems
- have gained familiarity with recent scientific literature in this field
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While the rise of the internet has helped strengthen the field of Information Retrieval (IR), the area stretches far beyond plain web search, as a discipline situated between information science and computer science. In 1968, Gerard Salton defined information retrieval as "a field concerned with the structure, analysis, organization, storage, searching, and retrieval of information". Even though the area has seen many changes since that time and made a tremendous impact (who has never used a search engine?!), that definition is still accurate.
IR takes the notion of "relevance" as its core concept. As the scope of IR is limited to those cases where computers try to identify the relevance of information objects given a user's information need (as opposed to humans doing that, the common scenario in information science), perhaps "Computational Relevance" would have been a better term for the research in this area.
In this course, we cover the following aspects of Information Retrieval:
- How do people search for information, and how can this be formalized?
- How can we take advantage of term statistics, structure and annotations to capture the meaning of texts?
- How can these elements be combined in order to find "relevant" information?
- What techniques are necessary to scale to large text collections?
- How can we empower information retrieval system using machine learning and neural network algorithms?
Instructional Modes
- Lecture
- Tutorial
- Self-study
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Participant of Information Retrieval should have the base qualifications as provided by the bachelor Computing Science, Information Science or Artificial Intelligence. This includes having a working knowledge of:
* Statistics
* Machine Learning and neural networks
* Programming
Programming experience is required for the pass/fail assignments.
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After a series of pass-fail assignments, you carry out a project. Passing the assignments is necessary to pass the course.
The final grade is determined by:
* Written exam (50%)
* Project (50%)
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