Machine Learning Summer School

COURSE SYLLABUS, third-cycle courses

ECTS/HP: 1.5 ECTS per week (can be taken up to three times)

Course code:
SRT0009

Educational level: third-cycle course

Entry requirements:

• Basic knowledge in Machine Learning
• Admission to third-cycle studies involving Machine Learning or Machine Learning applications

Course content:
The course involves the participation at a summer school with a subject in Machine Learning or Applications of Machine Learning. Summer schools, such as the International Deep Learning Summer School, the Summer School on Document Analysis and Application, or the Pattern Recognition Summer School are offered at least every other year in the international Scientific Community. The participation in such summer schools is an important part of PhD studies.

Learning outcomes:

• Deepen the knowledge in a specific area of machine learning, including the knowledge about recent research trends and open issues
• demonstrate the ability to plan and use appropriate methods to undertake a limited piece of research and other qualified tasks within predetermined time frames in order to contribute to the formation of knowledge as well as to evaluate this work
• demonstrate knowledge and understanding in the research domain including current specialist knowledge in a limited area of Machine Learning

Course methods:
The student has to participate in an officially summer school (national or international) and earn the certificate for that summer school. The summer school needs to involve knowledge acquisition (e.g., via presentations or tutorials) as well as practical individual tasks, where the students apply the knowledge on a piece of research or another qualified tasks (e.g., programming)

Examination form:
Written report of the summer school; a certificate; and a presentation in front of members of the Machine Learning Group.

Grading scale: Pass/Fail

Course literature:
Recent Summer School in machine learning and applications (to be decided together with and approved by a senior member of the Machine Learning Group).

Education cycle:
Any stage of PhD studies

Course is given periodically: Continuously

Send application to: any senior member of the machine learning group
Doctoral student enter name, civic registration number, e-mail, Division and Department in the application

Deadline for application:
continuous participation possible

Course open for application by doctoral students admitted to other universities than LTU: Yes □

Limited number of students: No □

Tuition:
If the course is allocated resources via internal resource allocation system, the course is free of charge for doctoral students admitted at LTU, for doctoral students admitted to other universities, course fees may be required. For other courses the Department decides on course fees.

Contact person:
Rajkumar Saini (Rajkumar.Saini@ltu.se)

Examiner:
Marcus Liwicki

Course syllabus decided by:
Johan Carlson, Forskarutbildningsledare SRT, LTU-2639-2026

Date of decision: 2026-03-24