Rules for passing the course

• The primary task of the course is to assist doctoral students in applying machine learning methods to their specific research work. Accordingly, the class will follow two introductory lectures with labs combined with individual consultations. During the labs, doctoral students will be tasked with selecting an appropriate research problem from their field of science and conducting a simple mini-project involving machine learning. The course will end with a seminar, during which each doctoral student will give a short presentation summarizing the work done during the course and its further possible directions. The grade for the course will be given on the basis of the presentation (taking into account the work during the semester). The form and visual layer of the presentation, the merit of the speech, and its duration are evaluated. Prior to the seminar, presentations must be uploaded to the ePortal platform.
• If a doctoral student did not show up for a seminar class or received a failing grade in the class, but was present at 50% of the course meetings, he or she is entitled to an additional attempt to pass the course. This approach will be in the form of an individual discussion on topics related to the course. In this case, the maximum possible grade is 3.0.
• If a doctoral student did not show up for a seminar class and at the same time did not attend 50% of the course meetings, he/she loses the right to present and receives a failing grade.

Interdisciplinary_applications_of_AI.docx

Rules of attendance, limit of permissible absences from classes, the procedure for excusing absences and how to make up for them

Attendance is not required. However, it is worth remembering that the delivery of the final speech is required to pass the course, and by skipping the meetings, the doctoral student waives the right to receive assistance from the instructor in the process of preparing for the presentation. Rules for passing the course apply.

Rules for informing about grades

Grades will be recorded in the USOS system after the last meeting.

Student hours

https://www.kssk.pwr.edu.pl/users/zyblewski and the teacher’s USOS profile.

Teacher surveys

PZYBL.docx

PKSIE.docx

JKLIK.docx