DD2412 Deep Learning, Advanced Course 6.0 credits

Djupinlärning, fortsättningskurs

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

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Offering and execution

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Course information

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

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Literature and preparations

Specific prerequisites *

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

Recommended prerequisites

DD2424

Equipment

No information inserted

Literature

Information about the course literature will be announced in the course memo.

Examination and completion

Grading scale *

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

Examination *

  • LAB1 - Laboratory work, 3.0 credits, Grading scale: P, F
  • TEN1 - Project, 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.

The examiner decides, in consultation with KTH's coordinator for disabilities (Funka), about possible adapted examination for students with documented, permanent disabilities. The examiner may permit other examination format for re-examination of individual students.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

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Examiner

Hossein Azizpour

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 DD2412

Offered by

EECS/Intelligent Systems

Main field of study *

Computer Science and Engineering

Education cycle *

Second cycle

Add-on studies

No information inserted

Contact

Hossein Azizpour (azizpour@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

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex