Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN428Information Retrieval3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The aim of the course is to provide an introduction to the basic principles and techniques used in Information Retrieval (IR); to demonstrate how statistical models of language can be used to solve the document retrieval problem.
Course Content Basic and advanced techniques for text-based information systems, text indexing, Boolean and vector space retrieval models, language models, tolerant retrieval, evaluation and interface issues, web crawling and link-based algorithms, clustering and LSI, neural models for retrieval.
Course Methods and Techniques Lecture
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 )
Course Coordinator None
Name of Lecturers Prof. Ebru Akçapınar Sezer
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources C. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval, Cambridge University Press, 2008. W. Bruce Croft, Donald Metzler, and Trevor Strohman Search Engines: Information Retrieval in Practice. Pearson, 2010.
Course Notes C. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval, Cambridge University Press, 2008.

W. Bruce Croft, Donald Metzler, and Trevor Strohman Search Engines: Information Retrieval in Practice. Pearson, 2010.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 1 % 20
Assignment 2 % 20
Project 1 % 30
Final examination 1 % 30
Total
5
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 12 2 24
Assignments 2 20 40
Project 1 40 40
Preparation for Midterm Exam 1 16 16
General Exam Preparation 1 18 18
Total Work Load   Number of ECTS Credits 6 180

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 to gain an understanding of the basic concepts and techniques in Information Retrieval;
2 to understand how statistical models of text can be used to solve problems in IR, with a focus on how the vector-space model and the language model can be applied to the document retrieval problem;
3 to understand how statistical models of text can be used for other IR applications, for example clustering;
4 to appreciate the importance of data structures such as an index to allow effic
5 Bir belge erişim sistemi oluşturma deneyimine sahip olmak.
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Information Retrieval
2 Boolean Model, Text Pre-Processing, Inverted Indexes
3 Approximate String Matching and Tolerant Retrieval
4 Index Construction and Compression
5 Vector Space Model; Text Similarity Metrics; Term Weighting, Ranked Retrieval
6 Evaluating Information Retrieval
7 Relevance Feedback; Query Expansion
8 Midterm exam
9 Language Models for Information Retrieval
10 Text Classification and Clustering
11 Lexical Semantics and Wordnet
12 Latent Semantic Indexing
13 Web Search and Crawling
14 Link Analysis
15 Preparation to Final Exam
16 Final Exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 4 4 3 4 4 1 1 3 3 2 2 3
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://bilsis.hacettepe.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2733660&lang=en