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EN2202 Pattern Recognition 7.5 credits

Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus EN2202 (Autumn 2010–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course is about the theoretical foundation of pattern recognition and gives an introduction to technical applications, especially in speech recognition and image or sound classification.

Intended learning outcomes

The participants shall after the course be able to

* design systems and algorithms for pattern recognition (signal classification), with focus on sequences of patterns that are analyzed using, e.g., hidden Markov models (HMM),

* analyse classification problems probabilistically and estimate classifier performance,

* understand and analyse methods for  automatic training of classification systems,

* apply Maximum-likelihood parameter estimation in relatively complex probabilistic models, such as mixture density models and hidden Markov models,

* understand the principles of Bayesian parameter estimation and apply them in relatively simple probabilistic models.

Literature and preparations

Specific prerequisites

For single course students: 120 credits and documented proficiency in English B or equivalent

Recommended prerequisites

  • SF1901 Probability Theory and Statistics, or equivalent.
  • EQ1220 Signal Theory or equivalent is recommended but not required.


No information inserted


Arne Leijon (20xx) Pattern Recognition. KTH. (latest version)

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


  • INL1 - Home Work, 2.5 credits, grading scale: A, B, C, D, E, FX, F
  • TEN1 - Examination, 5.0 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.

Written exam and compulsory Homework Assignment including Matlab implementation of classifier tools.

Other requirements for final grade

Exam 5p (grade A-F). Homework Assignment 2.5p (A-F). The final grade is a weighted sum of graded performance on the Exam and Homework Assignment, with weight 25 for the exam and 10 for the Homework Assignment.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

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

Add-on studies

No information inserted


Saikat Chatterjee

Supplementary information

All course material in English. Student assignment reports in either Swedish or English.