A seminar course on "Understanding vision: theory, models, and data", University of Tuebingen, April-July 2021 --- will use remote video conferencing for lectures if necessary.

Please email me ASAP (best no later than one week lecture starts) at li.zhaoping@tuebingen.mpg.de if you are thinking of joining the course (whether for credit or just for auditing), I like to make a mailing list for communications on, e.g., lectures by video conferencing and the selection of topics for the course In addition, if you like to take this course as a student from the University, you can find this course in the ALMA system in the University.

Course schedule: Lecture: every Friday 10:00-12:00, Tutorials: Wednesday 12:00-14:00 and Thursday 10:00-12:00. The two tutorials are to help you prepare (reading, abstract writing, course presentation, etc) for the Friday lecture of the week. The abstract that you have to write for the week is due on 10 am Thursday (at the second tutorial of the week). The first tutorial can help your reading before the abstract writing, The second tutorial can help you to get feedback for your written abstract, before the lecture on Friday.

A short course description: This course is based on the book Understanding vision: theory, models, and data , (see table of contents of the book ). Each week will cover a selected part of the book. There will be 2 hours of lectures and two tutorial sessions each week. Before the 2-hour lecture, each student should complete some assignments in order prepare for the lecture, and the tutorial sessions are designed to help students with these assignments. The assignments are as follows. Each student should read the selected part of the book for the week and write an abstract, due at 10 am on Thursday at the beginning of the second tutorial of the week. The abstract describes a summary of his/her understanding of this part. It should have 300-500 words, and should include at least one question or one comment on the material. In addition, the students need to answer some multiple choice short questions related to the materials read, for the purpose of better digestion of the materials needed before the lecture. During the 2-hour lecture, one or two of the students will give a presentation of the material for the week. Students take turns to give the presentations across the weeks of the semester. For parts of the book that include mathematical materials, the presentation should be such that each essential point should be presented in two ways, one mathematically and one intuitively and conceptually, use figures and graphics to illustrate the point whenever it is helpful. There will be class discussions during and after the presentation, guided by the lecturer. The student presenters for the week will also be helped by the lecturer and the TAs for preparing the presentation during the tutorials (and additional if needed).

Course grades: click here for details

Some video lectures: Here are A few video lectures and a growing playlist on the content of the book.

Course textbook is Understanding vision: theory, models, and data . You can borrow this book from the university library and the GTC library.

Here is a little intro (adapted from Amazon.com) to the book "Understanding Vision": it explains the computational principles and models of biological visual processing, and in particular, of primate vision. It is written in such a way that vision scientists, unfamiliar with mathematical details, should be able to conceptually follow the theoretical principles and their relationship with physiological, anatomical, and psychological observations, without going through the more mathematical pages. For those with a physical science background, especially those from machine vision, this book serves as an analytical introduction to biological vision. It can be used as a textbook or a reference book in a vision course, or a computational neuroscience course for graduate students or advanced undergraduate students. It is also suitable for self-learning by motivated readers (students and researchers in computational neuroscience, vision science, machine and computer vision, as well as physicists interested in visual processes).

Course pre-requisite: 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 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 in the past.

Location: zoom lectures (email the lecture for the zoom link and join the course mailing list) while social distancing still holds, otherwise typically in room 203 of Max Planck Ring 8, in Max Planck Institute for Biological Cybernetics.

Lecturers: Prof. Li Zhaoping (lead lecturer), and Teaching assistant and colleagues from Zhaoping's group.

Preliminary lecture content schedule , here you can see which topics are divided into lecture components (which week) for this course.

Course readings, preparation before each lecture:

To prepare for the first lecture ( on April 23rd), you need to do the following: