Syllabus for DTM-534
Information Retrieval
This course explores the theories and methods used to search and retrieve text and bibliographic information from document repositories. Information retrieval focuses on the analysis of relevance and utility of information. The course will explore data organization and representation, information access techniques, categorization, content analysis, data structures used for unstructured data, indexing and indexes, clustering and classification methodologies, search and navigation techniques, and search engines. In this course, students will learn to use statistical and linguistic methods for automatic indexing and classification, Boolean and probabilistic approaches to indexing, query formulation, and output ranking. In addition, students will learn to develop and analyze various data filtering methods, measures of retrieval effectiveness, and retrieval methodologies.
After completing this course, students should be able to:
CO1 Evaluate methods for organizing information.
CO2 Assess how indexing works within a search engine environment.
CO3 Evaluate text processing and search and navigation techniques.
CO4 Compare and contrast different approaches to query processing.
CO5 Evaluate document ranking and output ranking.
CO6 Critique methods of compression and data filtering.
CO7 Analyze information retrieval models and data structures for unstructured data.
You will need the following materials to complete your coursework. Some course materials may be free, open source, or available from other providers. You can access free or open-source materials by clicking the links provided below or in the module details documents. To purchase course materials, please visit the University's textbook supplier.
ISBN: 978-0262528870
Information Retrieval is a three-credit, online course consisting of six modules. Modules include an overview, topics, learning objectives, study materials, and activities. Module titles are listed below.
For your formal work in the course, you are required to participate in online discussion forums, complete practice exercises and written assignments, and submit a final project. See below for details.
Consult the Course Calendar for due dates.
One or more of your course activities may utilize a tool designed to promote original work and evaluate your submissions for plagiarism. More information about this tool is available in this document.
You are required to participate in six discussion forums. The discussion forums are on a variety of topics associated with the course modules. There is also an ungraded but required Introductions Forum. A grading rubric for the discussion forums can be found in the Evaluation Rubrics folder.
You are required to complete six written assignments. The written assignments are on a variety of topics associated with the course modules. Grading rubrics for the written assignments can be found within the assignment submission links in each module.
You are required to complete six practice exercises. The practice exercises are on a variety of topics associated with the course modules. Grading rubrics for the practice exercises can be found within the assignment submission links in each module.
You are required to complete a final project for which you will be proposing a new comprehensive IR algorithm/approach for large data. Details can be found in the Final Project area of the course. A grading rubric for the final project can be found within the assignment submission link.
Your grade in the course will be determined as follows:
All activities will receive a numerical grade of 0–100. You will receive a score of 0 for any work not submitted. Your final grade in the course will be a letter grade. Letter grade equivalents for numerical grades are as follows:
A | = | 93–100 | B | = | 83–87 | |
A– | = | 90–92 | C | = | 73–82 | |
B+ | = | 88–89 | F | = | Below 73 |
To receive credit for the course, you must earn a letter grade of C or higher on the weighted average of all assigned course work (e.g., assignments, discussion postings, projects, etc.). Graduate students must maintain a B average overall to remain in good academic standing.
To succeed in this course, take the following first steps:
Consider the following study tips for success:
To ensure success in all your academic endeavors and coursework at Thomas Edison State University, familiarize yourself with all administrative and academic policies including those related to academic integrity, course late submissions, course extensions, and grading policies.
For more, see:
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