- Basic theory of deep networks
- Probabilistic deep learning
- Types of learning in deep learning
- Deep learning with imperfect labels
- Reliable deep learning
DD2610 Deep Learning, advanced course 7.5 credits

Information per course offering
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
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
Master's Programme, Computer Science, åk 2, CSDA, Recommended
Master's Programme, Cybersecurity, åk 1, Recommended
Master's Programme, Cybersecurity, åk 2, Recommended
Master's Programme, Industrial Engineering and Management, åk 1, MAIG, Conditionally Elective
Master's Programme, Machine Learning, åk 2, Conditionally Elective
Master's Programme, Systems, Control and Robotics, åk 2, LDCS, Conditionally Elective
Master's Programme, Systems, Control and Robotics, åk 2, RASM, Conditionally Elective
Contact
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–)Content and learning outcomes
Course contents
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
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
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
Offered by
Main field of study
Education cycle
Supplementary information
Overlaps with DD2412.
In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex