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DD2610 Deep Learning, advanced course 7.5 credits

Information per course offering

Termin

Information for Autumn 2025 Start 25 Aug 2025 programme students

Course location

KTH Campus

Duration
25 Aug 2025 - 12 Jan 2026
Periods
P1 (4.5 hp), P2 (3.0 hp)
Pace of study

25%

Application code

51868

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

Open for students from year 3 and students admitted to a master's programme as long as it can be included in your programme.

Planned modular schedule
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Schedule
Schedule is not published

Contact

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

Course syllabus as PDF

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

Course syllabus DD2610 (Autumn 2025–)
Headings with content from the Course syllabus DD2610 (Autumn 2025–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Basic theory of deep networks
  • Probabilistic deep learning
  • Types of learning in deep learning
  • Deep learning with imperfect labels
  • Reliable deep learning

Intended learning outcomes

On completion of the course, the student should be able to

  • explain and justify the subareas of deep learning
  • account for the theoretical background for advanced deep-learning techniques
  • identify needs of additional research in the area
  • implement several deep-learning methods based on new research
  • analyse advanced research in the area and critically evaluate the methods' weaknesses and strengths

in order to:

  • be prepared for a degree projects/postgraduate studies in deep learning
  • better be able to meet the industry's need for cutting-edge competence in the area.

Literature and preparations

Specific prerequisites

Knowledge in basic deep learning, 7.5 higher education credits, equivalent completed course DD2424 or DD2437.

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

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

Examination

  • LAB1 - Laboratory work, 4.5 credits, grading scale: P, F
  • PRO1 - Project work, 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.

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

Computer Science and Engineering

Education cycle

Second cycle

Supplementary information

Overlaps with DD2412.

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