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SF1930 Statistical Learning and Data Analysis 6.0 credits

The course gives an introduction to modern statistical inference and learning.

Information per course offering

Termin

Information for Autumn 2025 Start 25 Aug 2025 programme students

Course location

KTH Campus

Duration
25 Aug 2025 - 24 Oct 2025
Periods

Autumn 2025: P1 (6 hp)

Pace of study

50%

Application code

50609

Form of study

Normal Daytime

Language of instruction

Swedish

Course memo
Course memo is not published
Number of places

Places are not limited

Target group
Only CTMAT 2
Planned modular schedule
No information inserted

Contact

Course syllabus as PDF

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

Course syllabus SF1930 (Autumn 2021–)
Headings with content from the Course syllabus SF1930 (Autumn 2021–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course gives an introduction to the theory of statistical inference and prediction, which constitute the main goals for modern statistical data analysis and machine learning. Particular attention is given to multidimensional probability distributions and exponential families, which are fundamental tools for modeling data analytical problems, and the theory of graphical models is a powerful means for describing conditional dependencies with a bearing on high-dimensional statistical inference problems. Decision theory provides a framework for making optimal decisions under statistical uncertainty, as well as weighting different statistical approaches against each other. In particular, Bayesian decision theory—in which the inference and learning problems are solved through calculation of the posterior and predictive distributions, respectively—plays a central role in today’s statistical data analysis and is used to construct Bayesian point estimates, hypothesis tests, and credibility intervals. In parallel with the Bayesian approach, likelihood theory is also discussed, and special attention is given to the asymptotic properties of the maximum likelihood estimate as the amount of data grows towards infinity. The course also introduces basic statistical computation methods, such as stochastic gradient methods and Markov chain Monte Carlo (MCMC) methods, which are of great importance in modern computer-intensive statistics. In the course, these are applied to real data-analytical problems within the framework of a computer-based project.

Intended learning outcomes

After having passed the course, the student is supposed to be able to:

  • formulate and apply concepts in statistical inference and prediction to solve theoretical problems;
  • formulate and apply concepts in statistical inference and prediction to solve problems in data analysis;
  • design and implement methods in statistical learning for data analysis.

Literature and preparations

Specific prerequisites

Completed basic course in probability and statistics (SF1918, SF1922 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 - Written exam, 4.0 credits, grading scale: A, B, C, D, E, FX, F
  • INL1 - Hand in assignment, 2.0 credits, grading scale: P, 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

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

Technology

Education cycle

First cycle