- Learning of representations from raw data: images and text
- Principles of supervised learning
- Elements for different methods for deep learning: convolutional networks and recurrent networks
- Theoretical knowledge of and practical experience of training networks for deep learning including optimisation using stochastic gradient descent
- New progress in methods for deep learning
- Analysis of models and representations
- Transferred learning with representations for deep learning
- Application examples of deep learning for learning of representations and recognition
FDD3424 Deep Learning in Data Science 7.5 credits
Information per course offering
Information for Spring 2024 Start 18 Mar 2024 programme students
- Course location
KTH Campus
- Duration
- 18 Mar 2024 - 3 Jun 2024
- Periods
- P4 (7.5 hp)
- Pace of study
50%
- Application code
60849
- 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 FDD3424 (Spring 2019–)Headings with content from the Course syllabus FDD3424 (Spring 2019–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
After the course, you should be able to:
- explain the basic the ideas behind learning, representation and recognition of raw data
- account for the theoretical background for the methods for deep learning that are most common in practical contexts
- identify the practical applications in different fields of data science where methods for deep learning can be efficient (with special focus on computer vision and language technology)
in order to:
- be able to solve problems connected to data representation and recognition
- be able to implement, analyse and evaluate simple systems for deep learning for automatic analysis of image and text data
- receive a broad knowledge enabling you to learn more about the area and read literature in the area
Literature and preparations
Specific prerequisites
No information inserted
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
P, F
Examination
- EXA1 - Examination, 7.5 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.
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
This course does not belong to any Main field of study.
Education cycle
Third cycle
Add-on studies
No information inserted