Probabilistic Graphical Models and their Applications Prof. Dr. Bernt Schiele

News

13.11.2020

Assignment 1

The first assignment has been released (in Materials). Due date: 25th Nov, 11:59pm.

11.11.2020

Office Hours for Tutors

Office 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

[Pgm_annoncements] mailing list

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... 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

Zoom link for the first lecture

 

https://zoom.us/j/94066494694?pwd=dDdLUElrVCswSDdDOWx4UmR4RVJ1UT09

Meeting ID: 940 6649 4694
Passcode: 547442

03.11.2020

Zoom link for the first exercise

 

https://zoom.us/j/97145007820?pwd=Yk9ZK1ZzTUdKWmljOWprK0Y2M0MxZz09

Meeting ID: 971 4500 7820
Passcode: 535352

Matlab installation: https://www.hiz-saarland.de/dienste/software-lizenzen/mathworks/ (with UdS campus license)
Instruction: Tutorial

 

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.



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