Advanced Topics in Computer Vision and Machine Learning
Summer Semester 2017
This seminar covers advanced topics in computer vision and statistical machine learning based on recent research publications. Exemplary topics include object recognition, scene understanding, video analysis, tracking, image modeling, image restoration, vision in robotics & driver assistance, graphical models, deep learning, feature learning, semi-supervised learning, and optimization in vision & learning.
|Event type||seminar (S2, 3CP, 2SWS), elective|
|Being taught||irregularly, usually summer semesters|
|Time||Mondays, 15:20 – 17:00|
|Location||S2|02, room C120|
|Lecturer||Prof. Stefan Roth, Ph.D.|
- Basics of scientific presentations and reviewing
- Independent familiarization with current publications in computer vision or machine learning (in English)
- Further research on background literature, with help from a mentor
- Preparation of a two-part slide presentation (problem statement and proposed solution) of one publication, with feedback from mentor
- Writing a scientific “mock” review of another publication, with aid from mentor
- Giving the presentation in front of a mixed audience
- Guiding the interactive discussion after both presentation parts
- Active participation in discussions, including feedback to presenters
After successfully completing the seminar, students are able to use recent scientific publications to become acquainted with current topics in computer vision and/or machine learning in an independent fashion. They can recognize the key contributions of the publications and are able to present them to a heterogeneous audience, taking into account good practices of scientific presentation. They can direct a scientific discussion following the presentation. Moreover, they are able to author a scientific review following common standards of the scientific review process.
Can be taken for credit toward BSc / MSc Informatik, MSc Visual Computing, MSc Autonome Systeme, BSc / MSc Computational Engineering and others. Students from other departments, e.g. Mathematik, Elektrotechnik, IST, or Physik are welcome, though academic credit may need to be arranged.
Basic knowledge of computer vision and/or machine learning, for example acquired through the courses Computer Vision I and/or Statistical Machine Learning (formerly Machine Learning: Statistical Approaches I)
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