Machine Learning Project Dissemination and Reproducibility

COURSE SYLLABUS, third-cycle courses

ECTS/HP: 2 ECTS (can be taken up to three times)

Course code:
SRT0007

Educational level: third-cycle course

Entry requirements:

• Basic knowledge in Machine Learning
• Programming knowledge
• Successful participation in the course Machine Learning Development and Deployment (ML-dev)
• Admission to third-cycle studies involving Machine Learning or Machine Learning applications

Course content:

The course focuses on the dissemination of machine learning tools to the general public or the issues of reproducibility in machine learning research. Such a dissemination activity could be the:

• The inclusion of the programmatic research outcomes in the ML-dev framework
• The presentation of the research outcomes in a professional video
• The demonstration of the research outcomes in a physical or virtual demonstrator, available to the public

Learning outcomes:

• demonstrate the ability in both national and international contexts to present and discuss research and research findings in speech and writing and in dialogue with the society in general
• improving and applying practical skills in machine learning related programming
• contribution to bigger software projects
• demonstrate the capacity to contribute to social development and support the learning of others both through research and education and in some other qualified professional capacity.

Course methods:
The Machine Learning Development Framework (ML-dev) comprises the general functionalities Machine Learning projects to implement algorithms and get projects going more efficiently. With the growing knowledge in the Machine Learning Group and the generally rapid development of machine learning architectures, it is important to include most recent methods in the framework. PhD and project outcomes need to be reproducible and available for the general public. This course covers the aspects of PhD skill development which go beyond the direct needs of a specific PhD research project, but are important for becoming individual researchers in the future, i.e., being able to integrate research results in frameworks and making them reproducible, and to disseminate research outcomes for the general public.

Examination form:
Contribution to one of the abovementioned goals; and a presentation in front of members of the Machine Learning Group and at least one stakeholder from the intended target group

Grading scale: Pass/Fail

Course literature:

to be decided together with and approved by a senior member in the Machine Learning subject.

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-2637-2026

Date of decision: 2026-03-24