DD2434 Machine Learning, Advanced Course 7.5 credits

Maskininlärning, avancerad kurs

A second course in machine learning, giving a broadened and deepened introduction to the area.

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Offering and execution

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Course information

Content and learning outcomes

Course contents *

Fundamentals of  the probabilistic approach

  • Linear regression 
  • Kernels 
  • Gaussian processes
  • Representation learning
  • Graphical models
  • Hidden Markov Models
  • Expectation-Maximization
  • Variational Inference

Intended learning outcomes *

After successfully taking this course, a student should be able to:

  • explain and justify several important machine learning methods,
  • account for several types of methods and algorithms used in the field, implement them using the book, and extend and modify them,
  • critically evaluate the methods’ applicability in new contexts and construct new applications,
  • follow research and development in the area.

Course Disposition

12 lectures

5 exercises

Literature and preparations

Specific prerequisites *

DD2431 Machine learning or the equivalent. SF1901 Probability Theory and

statistics or the equivalent.

Recommended prerequisites

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 "Pattern recognition and Machine Learning", Christopher Bishop

Examination and completion

Grading scale *

A, B, C, D, E, FX, F

Examination *

  • LAB1 - Labs, 4.0 credits, Grading scale: A, B, C, D, E, FX, F
  • TEN1 - Exam, 3.5 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.

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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Jens Lagergren

Pawel Herman

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 DD2434

Offered by

EECS/Intelligent Systems

Main field of study *

Computer Science and Engineering

Education cycle *

Second cycle

Add-on studies

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Jens Lagergren (jensl@kth.se)

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.

Supplementary information

Grading criteria are made available when the course starts.

In this course, the EECS code of honor applies, see: