Program Overview

Course Overview

The course SF2940, Probability Theory, is a 7.5 credit course that aims to acquaint students with basic concepts in probability theory, models, and solution methods applied to real problems.

Information per Course Offering

Termin

Autumn 2025

Information for Autumn 2025 CINEK3 m.fl. programme students

• Course location: KTH Campus
• Duration: 25 Aug 2025 - 24 Oct 2025
• Periods: Autumn 2025: P1 (7.5 hp)
• Pace of study: 50%
• Application code: 50309
• Form of study: Normal Daytime
• Language of instruction: English
• Course memo: Course memo is not published
• Number of places: Places are not limited
• Target group: No information inserted
• Planned modular schedule: P1: B1, E1, J1, B2.
• Schedule: Link to schedule
• Part of programme:
  • Master of Science in Engineering and in Education, year 5, TEDA
  • Master's Programme, Computer Science, year 2, CSTC
  • Degree Programme in Industrial Engineering and Management, year 3, TMAI, Mandatory
  • Master's Programme, Applied and Computational Mathematics, year 1, CSSE, Mandatory
  • Master's Programme, Computer Science, year 1, CSTC
  • Master's Programme, Computer Science, year 2, CSCS
  • Master of Science in Engineering and in Education, year 4, TEDA
  • Master's Programme, Computer Science, year 2, CSDA
  • Master of Science in Engineering and in Education, year 5, MAFY
  • Master's Programme, Applied and Computational Mathematics, year 1, Mandatory
  • Master's Programme, Machine Learning, year 1
  • Master's Programme, Biostatistics and Data Science, year 1, Mandatory
  • Degree Programme in Engineering Mathematics, year 3
  • Master's Programme, Machine Learning, year 2
  • Master's Programme, Machine Design, year 1
  • Master's Programme, Systems, Control and Robotics, year 2
  • Master's Programme, Mathematics, year 2
  • Master's Programme, Mathematics, year 1

Contact

• Examiner: Kevin Schnelli
• Course coordinator: Lukas Kristiansson Schoug
• Teachers: Lukas Kristiansson Schoug

Course Syllabus as PDF

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

Content and Learning Outcomes

Course Contents

• Probability spaces, random variables and their distributions, functions of random variables, expectation
• Independence, conditional probabilities, conditional expectation
• Probability and moment generating functions, characteristic function, sums of random variables
• Convergence of random variables, law of large numbers, central limit theorem
• Multivariate normal distribution and applications

Intended Learning Outcomes

The overall aim of the course is for students to become well-acquainted with basic probability theory concepts, models, and solutions methods applied to concrete problems. After passing the course, the students should be able to:

• formulate and explain central definitions, results, and theorems within probability theory
• systematically apply concepts and methods to independently solve basic problems within probability theory
• read and understand a mathematical text.

Literature and Preparations

Specific Prerequisites

• English B / English 6
• Completed basic course in probability theory and statistic (SF1918, SF1922 or equivalent).

Recommended Prerequisites

• Basic course in Multivariable Calculus (SF1626, SF1674 or equivalent)
• Basic course in Algebra and Geometry (SF1624 or equivalent)

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

• TEN1 - Examination, 7.5 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

Kevin Schnelli

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.

Offered by

SCI/Mathematics

Main Field of Study

Mathematics

Education Cycle

Second cycle