| Instructors: | Prof. Dr. Rainer Lienhart, Eva Hörster |
| Time Lecture: | Tue, 8:15-9:45 and Thu, 12:15-13:45, room 207 (Eichleitnerstr.); starts Oct., 14 2008 at 8:15am sharp |
| Time Exercise: | Wed, 12:15-13:45, room 202 (Eichleitnerstr.); first exercise will be on Oct, 29th 2008 Please register with LectureReg |
| Credits: | 6 SWS, Schein: yes, LP: 9 |
| Exam: | Thursday, 05 Feb 2009, 12:15am – 1:45pm, Rooms: 207 (Eichleitnerstr.)
In order to be admitted to the final exam, students are required:
|
| Multimedia Teilbereiche: | Multimedia-Methoden, Multimedia-Anwendungen |
| Synopsis: | The course addresses all aspects of computer algorithms that let a computer see, hear, learn, and understand audio-visual and multimedia data in the small and large scale. Small scale refers to individual media files or streams, while large scale refers to mining the web.
Mining media data is inherently a multidisciplinary field. Thus, the course will lay down the foundations of
The learned concepts will be illustrated by successful examples in practice. The accompanying exercises will contain some hands-on experiences. Towards the end of the course more advanced topics in object detection and object recognition will be addressed.
|
Important Comments
- All specified reading notes are relevant for the exams independent on how thoroughly they have been discussed during the lecture. Thus read them carefully.
Literature
- Mandatory reading: M. Mitchell. Machine Learning. McGraw-Hill Science/Engineering/Math; Chapters 1-8; (http://www-2.cs.cmu.edu/~tom/mlbook.html)
- Mandatory reading: Jeff Hawkins, Sandra Blakeslee. On Intelligence. B&T; Auflage: Reprint (August 2005), ISBN-13: 978-0805078534
- Bernd Jähne. Digital Image Processing. Springer Verlag.
- David A. Forsyth and Jean Ponce. Computer Vision: A Modern Approach. Prentice Hall, Upper Saddle River, New Jersey 07458.( http://www.cs.berkeley.edu/~daf/book.html )
- Martin Schader and Stefan Kuhlins. Programmieren in C++. Springer-Verlag. ISBN 3540637761
This is a perfect resource for all your questions relating C/C++; recommended if you are not skilled in C/C++
Online Material
| Date | Content | Slides | Exercise |
|---|---|---|---|
| 14.10. | 1-01 Introduction | ||
| 16.10. | 2-01 Introduction to Machine Learning | PDF ZIP | |
| 21.10. | 2-02 Concept Learning | ||
| 28.10. | 2-03 Decision Tree Learning | ||
| 30.10 | 2-04 Artificial Neural Networks | ||
| 04.11 | 2-05 Evaluating Hypotheses | ||
| 11.11. | 2-06 Bayesian Learning | PDF ZIP | |
| 13.11 | 2-07 Computational Learning Theory | ||
| 18.11 | 2-08 Instance Based Learning | ||
| 20.11 | 2-09 Reinforcement Learning | ||
| 25.11 / 27.11 / 02.12 | 3-01 Dimensionality_Reduction_Techniques | PDF Data | |
| 04.12 / 9.12 | 3-02 ShotDetection | ||
| 11.12 | 3-03 Scene_and_Locale_Detection | ||
| xx.yy | 3-04 Commercial Detection | ||
| xx.yy | 3-05 ObjectDetection | ||
| xx.yy | 3-06 Local Features | ||
| xx.yy | 3-07 Salient-Features | ||
| xx.yy | 3-08 Text Localization & Segmentation in Images, Web |