Summer Term 2020

Please note that only 'active' doctoral students of the GTC can participate in the courses listed.  Doctoral students who have not yet passed their admission interviews ('applicants') and guest students from other faculties can participate only in case of vacancies.

Courses indicated as 'Elective' will run throughout the semester and take place once a week. They are specialist courses offered to masters students, however, they might also be of interest to doctoral students working or planning to work in that particular field.

   course open - please register
   registration expired
Course title

Understanding vision: theory, models, and data (Elective)

Zhaoping Li
Course content / topics

This course is based on the book Understanding vision: theory, models, and data

( ), (see table of contents of the book here ).

Please send the lecturer an email if you are planning to take this course to, as the lecturer would like to create a mailing list to discuss and decide on the design of the course content/plan.

Please see updated course information at


Students from many departments (e.g., physics, math, computer science, psychology, neuroscience, biology)  can take this course.
Background knowledge on vision, or if you have no previous knowledge, please read chapter 1 and chapter 2 of the book: Understanding vision: theory, models, and data ( ) and speak with the lecturer  ( Prof. Dr. Zhaoping Li, ) for an OK. Background knowledge about vision science (including neuroscience and psychology of vision) could come from having taken a general course on perception, neuroscience, or sensory systems that includes vision as a topic, or from having taken a specific vision course. Better math skills (statistics, linear algebra, nonlinear dynamics, differential equations) will enable a student to get more from this course. However, with sufficient effort, students with limited math skills have successfully learned from the book on the past.

Students are required in the course to do the following:
​​​​​​​(1) attendance of the lectures
(2) read the reading materials before each lecture, submit a 300-500 word abstract based on the reading material, with at least one question or comment in this abstract on the material.
(3) when it is your turn, present the content of the reading material during the lecture as if teaching the material to your fellow students, and play a leading role in the discussions and answering questions related to the material.

At the end of the semester, there will be an written exam, the exam questions could be answered in a similar format as that for the weekly abstracts.

To earn 3 ECTS (whether by graded or pass/fail grading scheme), you need to do the following:
(1) Submit an abstract on the material for the lecture in each week, with 100-200 words for the abstract. The abstract could include one question or comment on the lecture material. These abstracts contribute 50% to the grade. Optional for trade off: you may choose to give one half-presentation to substitute for 5 weekly abstracts of your choice. An half-presentation is done by sharing the lecture presentation of a single week with another student. The course instructor will help with Q&A and discussions during and after the presentation. The course instructor will also offer guidance on lecture preparation. But you need to sign up for the presentation very soon to enable course logistic planning to make this option available to you.
(2) Lecture attendance, contributes 25% to the grade.
(3) Active participations in the Q&A and discussions in the lectures. This contributes 25% to the grade.

ILIAS Online Course Link

Day, time & location

SS20 online, link in course descricption. [Fri 10am-12, MPI Biol Cybernetics, room 203 (Max-Planck-Ring 8)]