Skip to main content
Before choosing courseEQ2340 Pattern Recognition 7.5 creditsAdministrate About course

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.

Course offering missing for current semester as well as for previous and coming semesters
* Retrieved from Course syllabus EQ2340 (Autumn 2015–)

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

No information inserted

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.

Equipment

No information inserted

Literature

No information inserted

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 hp, betygsskala: A, B, C, D, E, FX, F
  • TEN1 - Exam, 5,0 hp, betygsskala: 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

Examiner

Profile picture 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

Contact

Saikat Chatterjee sach@kth.se