News
17.09.2021
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Registration in LSF for the re-examDue to the fact that the winter semester is already closed, you can register for the re-exam under "Sommersemester 21 Termin 03". The results will be transferred to the winter semester afterwards. The date is 28.09, so you have to register / de-register before... Read more Due to the fact that the winter semester is already closed, you can register for the re-exam under "Sommersemester 21 Termin 03". The results will be transferred to the winter semester afterwards. The date is 28.09, so you have to register / de-register before 21.09.
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16.08.2021
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Re-exam, 28. SeptemberRe-exam will take place on September 28 Everybody who is interested in the re-exam should send an email to akukleva@mpi-inf.mpg.de The exact time slots will be announced a week before the exam.
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23.03.2021
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PGM exam on March 25Time slots were allocated and were sent individually to each student. If someone didn't get the time slot, write Anna (akukleva@mpi-inf.mpg.de).
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06.01.2021
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ProjectsYou are welcome to design your own project or adapt from the ones below. The deadline for the project is February 8. You will need to submit a report, the example you can find here. You are welcome to design your own project or adapt from the ones below. The deadline for the project is February 8. You will need to submit a report, the example you can find here.
Sample code to work with 3D human shapes: |
03.01.2021
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Assignment 3: deadline extensionDue to requests, the deadline for assignment 3 has been extended by a day to 5.01.21 (Tuesday) 11:59pm and an additional office hour (by Apratim) scheduled for 10am - 12pm on 4.01.21 (Monday). |
22.12.2020
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ExamExamination will be oral. Please register with LSF so the exam counts. We suggest two possible dates: 11 February and 25 March. The preferred date should be send as soon as possible by email to one of the tutors, and no later than 4 days before the... Read more Examination will be oral. Please register with LSF so the exam counts. We suggest two possible dates: 11 February and 25 March. The preferred date should be send as soon as possible by email to one of the tutors, and no later than 4 days before the exam. Final time slots will be assigned couple of days prior to the exam and send individually to students.
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14.12.2020
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Assignment 3The third assignment has been released (in Materials). Due date: 4th Jan (2021), 11:59 pm |
09.12.2020
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Assignment 2: deadline extensionThe deadline for the second assignment is extended till 13th December 11:59. |
27.11.2020
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Assignment 2The second assignment has been released (in Materials). Due date: 10th Dec, 11:59 pm
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13.11.2020
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Assignment 1The first assignment has been released (in Materials). Due date: 25th Nov, 11:59pm. |
11.11.2020
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Office Hours for TutorsOffice hours will be virtual via Zoom. Apratim Bhattacharyya (Wednesday 11am - 1pm) Anna Kukleva (Tuesday 2 pm - 4 pm)
zoom links in the materials |
10.11.2020
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[Pgm_annoncements] mailing listWe have added all course participants to the mailing list: https://lists.mpi-inf.mpg.de/listinfo/pgm_announcements If due to any reason, you have not received the welcome email, you can subscribe by clicking on the link above. This list will be used for... Read more We have added all course participants to the mailing list: https://lists.mpi-inf.mpg.de/listinfo/pgm_announcements If due to any reason, you have not received the welcome email, you can subscribe by clicking on the link above. This list will be used for important announcements e.g. zoom links for lectures. Please send an email to the tutors if you have any questions. |
03.11.2020
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Zoom link for the first lecture
https://zoom.us/j/94066494694?pwd=dDdLUElrVCswSDdDOWx4UmR4RVJ1UT09 Meeting ID: 940 6649 4694 |
03.11.2020
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Zoom link for the first exercise
https://zoom.us/j/97145007820?pwd=Yk9ZK1ZzTUdKWmljOWprK0Y2M0MxZz09 Meeting ID: 971 4500 7820 Matlab installation: https://www.hiz-saarland.de/dienste/software-lizenzen/mathworks/ (with UdS campus license) |
Probabilistic Graphical Models and their Applications
Overview
This course will introduce the basic concepts of probabilistic graphical models. Graphical Models are a unified framework that allow to express complex probability distributions in a compact way. Many machine learning applications are tackled by the use of these models, in this course we will highlight the possibilities with computer vision applications.
The main goal of the class is to understand the concepts behind graphical models and to give hands-on knowledge such that one is able to design models for computer vision applications but also in other domains. Therefore the lecture is roughly divided in two parts: learning about graphical models and seeing them in action.
In the first part of the lecture we will discuss the basics of solving these models, eg. for special kinds of graphs where efficient exact inference is possible and approimate methods for the general case. In the second part we will then discuss prominent applications for both low- and high-level computer vision problems. Some examples are statistical models of images (eg denoising), body pose estimation, person tracking, object detection and semantic image segmentation.
The exercises will be a mix of theoretical and practical assignments.