- Deep networks.
- Probabilistic deep learning.
- Deep transfer and sharing of knowledge.
- Unsupervised deep representation learning.
- Higher order learning.
- Adversarial learning.
DD2412 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.
About course offering
For course offering
Autumn 2024 DLAHT22 programme students
Target group
Open for students from year 3 and students admitted to a master's programme as long as the course can be included in the programme.
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 1, Conditionally Elective
Master's Programme, Machine Learning, åk 2, Conditionally Elective
Master's Programme, Systems, Control and Robotics, åk 1, Recommended
Periods
P1 (3.0 hp), P2 (3.0 hp)Duration
26 Aug 2024
13 Jan 2025
Pace of study
17%
Form of study
Normal Daytime
Language of instruction
English
Course location
KTH Campus
Number of places
Max: 120
Planned modular schedule
Course memo
Course memo is not publishedSchedule
Link to scheduleApplication
For course offering
Autumn 2024 DLAHT22 programme students
Application code
50250
Contact
For course offering
Autumn 2024 DLAHT22 programme students
Contact
Hossein Azizpour (azizpour@kth.se)
Examiner
No information insertedCourse coordinator
No information insertedTeachers
No information insertedHeadings with content from the Course syllabus DD2412 (Autumn 2024–) are denoted with an asterisk ( )
Content 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
Knowledge in deep learning, 6 credits, corresponding to completed course DD2424/DD2437.
Active participation in a course offering where the final examination is not yet reported in Ladok is considered equivalent to completion of the course.
Registering for a course is counted as active participation.
The term 'final examination' encompasses both the regular examination and the first re-examination.
Recommended prerequisites
DD2424
Equipment
No information inserted
Literature
No information inserted
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, 3.0 credits, grading scale: P, F
- PRO1 - Project assignment, 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.
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
Computer Science and Engineering
Education cycle
Second cycle
Add-on studies
No information inserted
Contact
Hossein Azizpour (azizpour@kth.se)
Transitional regulations
The former module TEN1 has been replaced by PRO1.
Supplementary information
In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex