- Deep networks.
- Probabilistic deep learning.
- Deep transfer and sharing of knowledge.
- Unsupervised deep representation learning.
- Higher order learning.
- Adversarial learning.
FDD3412 Deep Learning, Advanced Course 6.0 credits
![](https://kursinfostorageprod.blob.core.windows.net/kursinfo-image-container/Picture_by_MainFieldOfStudy_26_Default_picture.jpg)
The course goes beyond the basic principles of deep learning by delving into the frontiers of deep learning research.
Information for research students about course offerings
Autumn 19 P1
About course offering
For course offering
Autumn 2024 Start 26 Aug 2024 programme students
Target group
No information insertedPart of programme
No information insertedPeriods
P1 (3.0 hp), P2 (3.0 hp)Duration
Pace of study
17%
Form of study
Normal Daytime
Language of instruction
English
Course location
KTH Campus
Number of places
Places are not limited
Planned modular schedule
Course memo
Course memo is not publishedSchedule
Schedule is not publishedApplication
For course offering
Autumn 2024 Start 26 Aug 2024 programme students
Application code
50889
Contact
For course offering
Autumn 2024 Start 26 Aug 2024 programme students
Contact
Hossein Azizpour (azizpour@kth.se)
Examiner
No information insertedCourse coordinator
No information insertedTeachers
No information insertedContent and learning outcomes
Course contents
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
Recommended prerequisites
Completed course DD2424 Deep Learning in Data Science or DD2437 Artificial Neural Networks and Deep Architectures or the equivalent courses.
Equipment
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
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
Opportunity to raise an approved grade via renewed examination
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.