Basic theory of deep networks
Probabilistic deep learning
Types of learning in deep learning
Deep learning with imperfect labels
Reliable deep learning
FDD3610 Deep Learning, advanced course 7.5 credits
Information per course offering
Information for Autumn 2026 Start 24 Aug 2026 programme students
- Course location
KTH Campus
- Duration
- 24 Aug 2026 - 11 Jan 2027
- Periods
Autumn 2026: P2 (3 hp), P1 (4.5 hp)
- Pace of study
25%
- Application code
12667
- 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
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FDD3610 (Autumn 2025–)Headings with content from the Course syllabus FDD3610 (Autumn 2025–) are denoted with an asterisk ( )
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 industrv's need for cutting-edge competence in the area.
Literature and preparations
Specific prerequisites
No information inserted
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
Grading scale
P, F
Examination
- LAB1 - Laboratory work, 4.5 credits, grading scale: P, F
- PRO1 - Project, 3.0 credits, grading scale: P, F
Examination is based on a combination of assessments including quizzes, implementation practicals, and a final project.
Other requirements for final grade
The final grade will be based on the project grade
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