FUI's course evaluation

IN5310 - Advanced Deep Learning for Image Analysis

Assessment:

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What is your general impression of the course? What do you think of the course's difficulty level? What do you think of the amount of work required in relation to the number of credits given? What do you think about the scope of topics that were covered in this course? How often did you participate in the lectures? What is your general impression of the quality of the lectures? How important was the teaching method or methods to your learning and understanding of the course curriculum? What is your general impression of the quality of the teaching method(s)? What do you think of the difficulty level of the assesments? How important were the assignments/assessments to your learning and understanding of the curriculum? What do you think of the amount of work involved with completing the assessment? What did you think of the difficulty level of the graded assessment? Throughout the course, how did you experience the availability of the course teachers and/or group teachers if you had questions or found something unclear? Show all Hide

Summary:

Respondents’ overall impression of the course is very positive, with all respondents rating it from Quite good to Very good. The course is unanimously perceived as Difficult, and the overall workload is mostly described as Large, although the workload linked to graded assessments is seen as between Appropriate and High. Lectures are the main teaching activity and are attended by everyone, and group tuition is also used by most. Students report that they can almost always get in touch with teachers, and the quality of follow‑up and information is generally rated as Good to Very good. Continuous assessments and graded assessments are both viewed as Important to Very important for their learning.

Students particularly appreciate the quality of teaching, the structure and execution of coursework, and the choice of reading material. The content is experienced as interesting and relevant, and several comments highlight that the projects, topics and source book are engaging and helpful for learning.

Very good teaching and excellent execution of coursework,

I liked the source book and the topics of each lesson.

At the same time, students experience the level and pace as demanding. The course is perceived as difficult, and the weekly labs are described as “fast, and very difficult to keep up.” Some would like the teaching to be less focused on going through slides and more on deeper explanations and clearer distinction between what is central and what is less important, to make note‑taking easier. There are also comments that there is too much discussion in some activities, and that online channels can feel like a higher threshold for asking questions than in person.

Several concrete suggestions for improvement are given. Students propose making the course more applied, for example by replacing reading labs with quizzes and individual, smaller exercises that directly target the papers. Others suggest giving more time for assignments and adjusting the balance of topics by including more on neural networks and conformal prediction and less on kernel‑smoothing methods.

Give more time. Submit the assigments with more than 2 weeks before the deadline.

My suggestion is to replace the reading labs with questions from the reading (quizzes …), and then have weekly exercises that you do individually (smaller in size but more direct towards the papers).

About the course evaluation

The course evaluation is done by students in FUI (Fagutvalget ved Institutt for informatikk), and is based on questionnaires made at the end of each semester.