Social networks and multi-agent systems - course description

General information

Course name	Social networks and multi-agent systems
Course ID	11.3-WE-INFD-SNandM-AS-Er
Faculty	Faculty of Computer Science, Electrical Engineering and Automatics
Field of study	Computer Science
Education profile	academic
Level of studies	Second-cycle Erasmus programme
Beginning semester	winter term 2022/2023

Course information

Semester	2
ECTS credits to win	5
Course type	obligatory
Teaching language	english
Author of syllabus	dr inż. Jacek Bieganowski

Classes forms

The class form	Hours per semester (full-time)	Hours per week (full-time)	Hours per semester (part-time)	Hours per week (part-time)	Form of assignment
Lecture	30	2	-	-	Exam
Laboratory	30	2	-	-	Credit with grade

Aim of the course

To familiarize students with the genesis, architecture and properties of social networks. Big Data and the role that social networks play in the context of large-scale data generation. Developing basic skills in the analysis of media and social networks using Big Data technology. Introduction to multi-agent systems used for modeling social networks.

Prerequisites

Databases, Basic knowledge of statistics, Ability to program in Java, Knowledge of Big Data technology

Scope

Multi-agent systems as modern tools for distributed intelligence systems engineering. The use of multi-agent systems to build autonomous control mechanisms in the context of cloud computing. Definition of media and social networks. Types of social networks and characteristics of their functioning. Social media and Big Data as new trends setting the direction of IT development. Acquiring data from social media and their analysis using Big Data technology. Application of machine learning algorithms for advanced analysis of data obtained from social media.

Teaching methods

lecture: Conventional lecture, discussion, problem lecture,

laboratory exercises: teamwork, group work,

project: project method, group work, brainstorming

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

Students are assessed on the basis of:

Own project (50% of the grade) - the project verifies the achievement of learning outcomes in terms of practical skills. The project should include the implementation of the selected design task with documentation.

An exam (50% of the grade) of a written or oral nature. Students are admitted to the exam on condition that they receive credit for laboratory exercises during which their practical ability to perform tasks useful during the implementation of group projects will be assessed.

Notes

Opracował: dr inż. Mariusz Jacyno, dr inż. Jacek Bieganowski

Modified by dr inż. Jacek Bieganowski (last modification: 22-04-2022 00:08)

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