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DD2412 Deep Learning, Advanced Course 6.0 credits

The course goes beyond the basic principles of deep learning by delving into the frontiers of deep learning research.

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.


For course offering

Autumn 2024 DLAHT22 programme students

Application code


Headings with content from the Course syllabus DD2412 (Autumn 2024–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Deep networks.
  • Probabilistic deep learning.
  • Deep transfer and sharing of knowledge.
  • Unsupervised deep representation learning.
  • Higher order learning.
  • Adversarial learning.

Intended learning outcomes

After passing the course, the students should be able to:

  • explain and justify the subareas of deep learning,
  • account for the theoretical background for advanced deep learning techniques,
  • identify the directions in which additional research can be made to develop the field,
  • implement methods based on recently published results,
  • analyse advanced research in the area and critically evaluate the methods' weaknesses and strengths

in order to

  • prepare for degree project/postgraduate studies in deep learning,
  • become better trained to meet industry's need of key competence in the area.

Literature and preparations

Specific prerequisites

Knowledge in deep learning, 6 credits, corresponding to completed course DD2424/DD2437.

Active participation in a course offering where the final examination is not yet reported in Ladok is considered equivalent to completion of the course.
Registering for a course is counted as active participation.
The term 'final examination' encompasses both the regular examination and the first re-examination.

Recommended prerequisites



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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


  • LAB1 - Laboratory work, 3.0 credits, grading scale: P, F
  • PRO1 - Project assignment, 3.0 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.

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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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

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

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Hossein Azizpour (

Transitional regulations

The former module TEN1 has been replaced by PRO1.

Supplementary information

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