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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. 

Information per course offering

Termin

Information for Autumn 2024 Start 26 Aug 2024 programme students

Course location

KTH Campus

Duration
26 Aug 2024 - 13 Jan 2025
Periods
P1 (3.0 hp), P2 (3.0 hp)
Pace of study

17%

Application code

50889

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Places are not limited

Target group
No information inserted
Planned modular schedule
[object Object]
Schedule
Schedule is not published
Part of programme
No information inserted

Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted
Contact

Hossein Azizpour (azizpour@kth.se)

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus FDD3412 (Spring 2019–)
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.

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.

Equipment

No information inserted

Literature

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

Examination

  • 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

No information inserted

Examiner

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

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted

Contact

Hossein Azizpour (azizpour@kth.se)

Postgraduate course

Postgraduate courses at EECS/Robotics, Perception and Learning