FDD3319 Generative machine learning through learning by teaching 4.5 credits

The students and the lecturer meet at a number of two-hour sessions throughout the semester(approximately weekly during one period), at which they discuss topics relevant to the course.

At the start, the sessions are led by the lecturer, whilst a designated student for each session writes lecture notes based on the lecture contents and discussion, and also creates with Canvas quiz questions on the material. This material is shared with other students for them to study, pass, and refine further.

Later lessons are led by the students, who each select a specific topic within the course, digest a list of reading materials from the lecturer and course students into a slides-based lecture with associated Canvas questions on their selected topic, which they deliver to the other students. Students and lecturers work together to piece together a bigger picture of concepts and methods (with their strengths and weaknesses) in the field.

Participants must be doctoral students, but in the interest of attracting early-stage PhD students we do not require any specific prerequisite courses.

• DEL1 - Active participation, 1.5 credits, grading scale: P, F
• UND1 - Peer Teaching, 3.0 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.

If the course is discontinued, students may request to be examined during the following two academic years.

• 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.