EQ2340 Pattern Recognition 7.5 credits


How can you make a computer understand your voice? How can you make a computer understand your handwriting? How do you detect signal patterns that are hidden in noise? How can a computer distinguish between ECG recordings from healthy and sick hearts? The course in Pattern Recognition gives you the theory to answer this kind of questions. In the course project, you create your own MatLab toolbox for pattern recognition.

Offering and execution

Course offering missing for current semester as well as for previous and coming semesters

Course information

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.

Course Disposition

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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.


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

See course homepage for current information.

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 *

  • INL1 - Assignment, 2.5 credits, Grading scale: A, B, C, D, E, FX, F
  • TEN1 - Exam, 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

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Opportunity to raise an approved grade via renewed examination

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Saikat Chatterjee

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 web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web EQ2340

Offered by

EES/Information Science and Engineering

Main field of study *

Electrical Engineering

Education cycle *

Second cycle

Add-on studies

No information inserted


Saikat Chatterjee sach@kth.se