Skip to main content
Till KTH:s startsida Till KTH:s startsida

EP271V Internet of Things and Artificial Intelligence 7.5 credits

Administer About course

The Internet of Things (IoT) and Artificial Intelligence (AI) are technologies that make “the world of everything connected” because they can connect physical objects, people, or infrastructure, especially thanks to wireless communications. IoT and AI allow us to gather data, make data analysis, and ultimately take some automatic decisions. We will use IoT and AI in the digitalized world, where vehicles will drive without human drivers, and energy and water resources will be distributed much more efficiently than today. This course gives an overview on the most important aspects of wireless networking and machine learning for AI over IoT.

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Application

For course offering

Autumn 2024 Start 26 Aug 2024 single courses students

Application code

10123

Headings with content from the Course syllabus EP271V (Autumn 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The focus of the course is on machine learning methods and algorithms for communication protocols of Internet of Things (IoT). The course starts with an introduction of applications of network architecture. Thereafter, methods for communication protocols are treated and how these methods can be applied in the design of important aspects of the communication protocol stack. The course analyses machine learning algorithms that can be run on IoT systems, where data and calculations are distributed.

Intended learning outcomes

After passing the course, the student should be able to:

  • give an account of the central tools in communication technology for Internet of Things (IoT)
  • design IoT systems
  • give an account of central machine learning methods for IoT
  • design machine learning methods for IoT systems

in order to:

  • understand and explain which design options there are for a specific communication system
  • be able to provide arguments for which type of performance that should be prioritised in the design of IoT systems and machine learning methods
  • understand and explain machine learning design options for specific communication systems.

Literature and preparations

Specific prerequisites

  • In total 180 higher education credits of which at least 90 higher education credits in electrical engineering, engineering physics or technical
  • mathematics.
  • Knowledge in one variable calculus, 6 higher education credits.
  • Knowledge in computer communication, 6 higher education credits.
  • Knowledge in probability theory, 6 higher education credits.
  • The upper secondary course English B/6

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F

Examination

  • LAB1 - Laboratory task, 2.0 credits, grading scale: A, B, C, D, E, FX, F
  • LAB2 - Laboratory task, 2.0 credits, grading scale: A, B, C, D, E, FX, F
  • LAB3 - Laboratory task, 2.0 credits, grading scale: A, B, C, D, E, FX, F
  • PRO1 - Project work, 1.5 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Profile picture Carlo Fischione

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

EECS/Computer Science

Main field of study

Electrical Engineering

Education cycle

Second cycle

Add-on studies

No information inserted