Subject

Web search engines

Course Title

Web search engines
Web search systems

Code

F23L3S080

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

8 / Summer

Number of ECTS credits

Teacher

Ivan Kitanovski, Ivica Dimitrovski

Prerequisites for enrolling in the course

Artificial Intelligence or Introduction to Data Science or Machine Learning

10.

Objectives of the course programme (competences)

Introduction to concepts for the development of web search engines. Understanding the methods for processing queries and the document collections searched, as well as methods for automatically collecting data from the web. Upon completion of the course, the student is expected to demonstrate knowledge of methods for query processing, document representation, and their indexing and classification; to demonstrate knowledge of image search and indexing methods; and to be able to independently develop search algorithms using programming tools.

11.

Course content

Lectures:
1. Introduction to Web Search Engines.
2. Processing questions; Search with feedback.
3. Vector spaces; Document structure; Creating indexes.
4. Evaluation of search systems.
5. Clustering and classification of documents.
6. Collecting information from the web and social networks and indexing it.
7. Personalized search.
8. Question Answering Algorithms.
9. Image search and indexing.
10. Image search and indexing.
11. Ethical challenges in information search: privacy, fake news detection, fair search.
12. Neural network and vector representation language models.

Practical Classes:
1. Overview of search libraries and tools. Overview of ElasticSearch.
2. Processing queries with ElasticSearch and Python.
3. Indexing data with ElasticSearch and Python.
4. Overview of evaluation metrics.
5. Implementation of clustering and document classification algorithms in Python.
6. Implementing a bot to retrieve data in Python.
7. Implementation of algorithms for personalized search in Python.
8. Implementation of question understanding and answering algorithms in Python.
9. Implementation of algorithms for image indexing and searching.
10. Implementation of algorithms for image indexing and searching.
11. Review of models for detecting fake news and fair search.
12. Overview of neural network–based models and various vector representations in the context of information retrieval.

12.

Learning methods

Lectures using presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defense of a project assignment and a seminar paper.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	0 points
17.4.	Final exam	70 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

Activities 15.2 and 16.1 have been completed.

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze \| Introduction to Information Retrieval \| Cambridge University Press \| 2008 2. Stefan Büttcher, Charles L. A. Clarke, Gordon V. Cormack \| Information Retrieval: Implementing and Evaluating Search Engines \| MIT Press \| 2016
22.2.	Additional literature No. Author Title Publisher Year