DD2412 Deep Learning, Advanced Course 6.0 credits

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

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.

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

DD2424

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

• 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.

Hossein Azizpour

• 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 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.

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