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EQ2250 Fundamentals for Information and Communications Engineering 7.5 credits

Information per course offering

Course offerings are missing for current or upcoming semesters.

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus EQ2250 (Autumn 2027–)
Headings with content from the Course syllabus EQ2250 (Autumn 2027–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course provides a foundation of mathematical principles and concepts that are needed for continued specialization within information processing and communications engineering.

  • Multivariate and Complex Gaussian distributions
  • Basic estimation and detection theory. Sufficient statistics, Cramér Rao bounds
  • Basic information theory.
  • Basic learning theory
  • Matrix algebra. Complex valued vectors/matrices, Singular Value Decompositions and QR Decompositions
  • Basics of optimization theory, Lagrange techniques, convexity.
  • Application examples from information processing and communication systems.

Intended learning outcomes

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

  • use mathematical and statistical tools and reasoning to analyse, and solve problems that appear in information and communications engineering

  • formulate a mathematical model that is applicable and relevant for a given problem in the area

  • use a given or individually formulated mathematical model for solving a given technical problem in the area and analyse the result and its reasonableness

with the aim of establishing a foundation in the mathematical principles and concepts required for further in-depth study in information processing and communication technology.

Literature and preparations

Specific prerequisites

Knowledge of single-variable calculus, 7.5 credits, corresponding to a completed course SF1625/SF1673/SF1685.

Knowledge of multivariable calculus, 7.5 credits, corresponding to a completed course SF1626/SF1674/SF1686.

Knowledge of linear algebra, 7.5 credits, corresponding to a completed course SF1624/SF1672/SF1684.

Knowledge of probability and statistics, 6 credits, corresponding to a completed course SF1910–SF1925/SF1935 or a completed exam component TEN1 within SF1910/SF1925/SF1935.

Knowledge of signals and systems corresponding to completed courses EQ1110+EQ1120 or a completed course II1303.

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

Grading scale

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

Examination

  • INL1 - Case Study, 1.5 credits, grading scale: P, F
  • INL2 - Case Study, 1.5 credits, grading scale: P, F
  • TEN1 - Written Exam, 4.5 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. If the course is discontinued, students may request to be examined during the following two academic years.

Examiner

No information inserted

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

Main field of study

Electrical Engineering

Education cycle

Second cycle