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FDD3412 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. 

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Headings with content from the Course syllabus FDD3412 (Spring 2019–) 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.

Course disposition

No information inserted

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

Completed course DD2424 Deep Learning in Data Science or DD2437 Artificial Neural Networks and Deep Architectures or the equivalent courses.


No information inserted


Uppgift om kurslitteratur meddelas i kurs-PM.

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 - Exam, 6.0 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

Passing grade on the laboratory work and passing grade on the final project.

Opportunity to complete the requirements via supplementary examination

No information inserted

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 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 FDD3412

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


Hossein Azizpour (

Postgraduate course

Postgraduate courses at EECS/Robotics, Perception and Learning