Syllabus

Distributed Computing

Code
GIK2NX
Points
7.5 Credits
Level
First Cycle
School
School of Information and Engineering
Subject field
Information Systems (IKA)
Group of Subjects
Informatics/Computer and Systems Sciences
Disciplinary Domain
Technology, 100%
This course can be included in the following main field(s) of study
Information Systems1
Microdata Analysis2
Progression indicator within (each) main field of study
1G2F
2G2F
Approved
Approved, 11 March 2021.
This syllabus is valid from 01 April 2021.
Download as pdf To course page

Learning Outcomes

The overall goal of the course is that students acquire knowledge and skills in collecting and processing data from a heterogeneous computerised environment including distributed sensor units, clients and servers.

After completing the course, students will be able to:

Knowledge and Understanding

  • Describe concepts related to software agents, multi-agent systems, and autonomous and distributed systems
  • Describe properties and concepts of distributed and parallel systems.

Skills and Abilities

  • Implement software agents with the support of multi-agent frameworks
  • Configure, manage and develop applications for computers with limited memory
  • Conduct data analysis related to distributed systems
  • Apply scientific approaches in the planning, design, implementation and presentation of quantitative studies

Evaluation Ability and Approach

  • Argue for selected methods and techniques in artificial intelligence, data science or statistical analysis for problem solving
  • Evaluate the results of analysed collected data and suggest improvements

Course Content

The course deals with distributed systems with diffuse components over networks that communicate and coordinate their actions by sending messages to each other. Other types of network and system models are also covered.

In addition, software agents and distributed systems that are realised in the practical programme development components of the course are treated. In this context, methods and techniques for artificial intelligence (AI) or data analysis are used and implemented by the student.

The course focuses on quantitative methods that are used during the problem-solving process, including planning, data collection, data transformation, data storage, data analysis and presentation.

Assessment

Tests (2 credits), laboratory reports (2.5 credits) and assignments (3 credits).

Forms of Study

Lectures, laboratory work, workshops

Grades

The Swedish grades U–VG.

Tests (U - VG), laboratory reports (U - G) and assignments (U - VG).

The final grade is determined by an overall weighting of the examination components included in the course.

Prerequisites

  • Artificial intelligence 7,5 credits and Statistical Analysis 7,5 credits or Data Science och Machine Learning 7,5 credits
  • Object-Oriented Design and Problem-Solving, 7.5 credits
  • Research Methodology 7.5 credits First Cycle
  • Database Systems, 7.5 credits First Cycle

Other Information

This course cannot be included in a degree that also includes the course Distribuerad databehandling och problemlösning med Java, IK2018.