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FEO3274 Pattern Recognition, Machine Learning and Data Analysis 12.0 credits

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For course offering

Spring 2024 Start 18 Mar 2024 programme students

Application code


Headings with content from the Course syllabus FEO3274 (Autumn 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Theoretical content: Bayes minimum risk criterion, maximum likelihood (ML),

maximum-a-posteriori (MAP), recognition for sequence of vectors, hidden Markov

model (HMM), graphical models, Gaussian process, expectation-maximization (EM),

approximate inference, variational Bayes, artificial neural network (ANN), back

propagation, vanishing gradient problem, deep learning, restricted Boltzman machines

(RBM), sparse representations, dictionary learning, convex optimization, greedy

methods, sparse kernel machines – relevance vector machine (RVM) and support

vector machine (SVM), graphical models, message passing, approximate message

passing, adaptive learning, online learning, learning over networks, doubly stochastic

networks, adaptation over networks.

Project content: Multimedia, gene sequence and financial data pre-processing, feature

extraction, and machine learning problems.

Intended learning outcomes

After the successful completion of course, the students should be able to

1. Identify and formulate recognition, learning and analysis problems given a


2. Design systems and algorithms. Critically compare the algorithms in a tradeoff

between complexity and performance. Finally report the results.

3. Implement algorithms through convex optimization and Bayesian learning


4. Contribute to the frontier research area.

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

Basic knowledge in linear algebra and probability theory


No information inserted


1. Pattern Recognition, Compendium by Arne Leijon and Gustav Henter.

2. Pattern Recognition and Machine Learning, by C.M. Bishop.

3. Deep learning methods and applications, by L. Deng and D, Yu.

4. Adaptation, learning and optimization over networks, by A.H. Sayed.

5. Sparse and redundant representations: from theory to applications in signal

and image processing, by M. Elad.

6. Advanced data analysis from an elementary point of view, by C.R. Shalizi.

7. Research paper handouts

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • EXA1 - Examination, 12.0 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.

Other requirements for final grade

1. Must need to pass the mid-term exam.

2. Must need to perform 3 given projects.

3. Must need to pass 2 master tests.

4. At-least 75% attendance in teaching classes.

5. Satisfactory performance in research project (preferably in own research area).

Expectation is to find innovative publishable research result and proper report.

6. Satisfactory performance in paper presentation.

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

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted


Saikat Chatterjee (

Postgraduate course

Postgraduate courses at EECS/Information Science and Engineering