Table of Contents

Welcome to the course website for “Multimedia Data Mining” SoSe 2017! This lecture gives an introduction to social multimedia processing and extracting information from images, videos, and other multimedia content. The focus will be on retrieval, search, and filtering of concepts, objects, and scenes.

  • Lecturers: Dr. Damian Borth, Prof. Andreas Dengel, and guest lecturer (Dr. Christian Schulze and Marco Schreyer)
  • Credits: 2C+1R, 4 credit points
  • Language: English
  • Level: Master (entry level)
  • Requirements
    • Introduction to Pattern Recognition (recommended, but not required)
    • Introduction to Image Processing and Image Understanding (recommended, but not required)
    • basic probability theory and analysis
  • Literature (available in computer science library):
    • Ian Goodfellow, Yoshua Bengio,Aaron Courville: Deep Learning, 2017
    • Duda, Hart, Stork: Pattern Classification, 2nd edition
    • Gonzalez, Woods: Digital Image Processing
    • Bishop: Pattern Recognition and Machine Learning
  • Exam: written or oral (to be discussed in class)


  • October 24th: The review session for the exam sheets (Einsicht) will take place on Friday, 27th October in Bayes(2.04) at DFKI between 3:00pm - 5:00pm
  • October 24th: Results of the second MDM Exam are now online. Please find the published results here
  • August 29th: The review session for the exam sheets (Einsicht) will take place on Friday, 1st September in Wittgenstein at DFKI between 3:30pm - 5:30pm
  • August 29th: Results of the first MDM Exam are now online. Please find the published results here
  • July 19th: Slides of last lecture are available online.
  • July 12th: last tutorial is dedicated for Q&A. It takes place on Wednesday 19th
  • July 12th: Slides of lecture-13 are available online.
  • July 6th: Dates and places of the exams can be found in KIS:
  • July 5th: Slides of lecture-12 are available online.
  • July 4th: Slides of lecture-11 are available online.
  • June 27th: Exercise sheet-05 is available online (exercise will take place July 5th)
  • June 13th: Slides of lecture-08 are available online.
  • June 12th: Exercise sheet-04 is available online (exercise will take place June 21st)
  • June 6th: Slides of lecture-07 are available online.
  • June 2nd: Slides of lecture-06 are available online.
  • May 29th: Please register for the written exam
  • May 16th: Solutions for exercise sheet-02 are available online. (“Ex2IPythonNotebook” in table below)
  • May 16th: NVIDIA GPU Tech Conference Keynote can be watched online: here
  • May 9th: Submit exercise sheets to
  • May 4th: Exercise sheet-02 is available online (exercise will take place May 10th)
  • May 2nd: Slides of lecture-02 are available online.
  • April 25th: Please note, there will be no lecture Tuesday May 2nd.
  • April 24th: First exercises is scheduled for Wednesday 8:15-9:45, Room: 36-265: Basic Math
  • April 18th: Lecture starts today!



  • Damian Borth
  • Contact email (all requests)
    • Managed by Tushar & Philipp
  • Registration for the class to help us organizing the class
    • please register with an email to providing the following information:
      • Name
      • email
      • field of study
      • do want to take the exam?


Lecture schedule:

block date lecturer content details slides & demos exercise sheets & materials
1 18.04.17 Damian Borth Introduction course formalities, introduction slides -
2 25.04.17 Damian Borth Multimedia Information Systems definitions, setup, labels, benchmarking slides sheet
3 02.05.17 Damian Borth No lecture - - Ipython/Jupyter Installation link
4 09.05.17 Marco Schreyer Pattern Recognition (supervised) basic decision theory, classification, classifier combination slides sheet material NumPy Website NumPy Tutorial Ex2IPythonNotebook
5 16.05.17 Damian Borth Pattern Recognition (unsupervised) clustering, dimensionality reduction slides -
6 23.05.17 Damian Borth Text Retrieval document retrieval, retrieval models, relevance feedback, data structures slides -
7 30.05.17 Damian Borth Image Representation definitions, features and their properties, invariance, global features, local features slides sheet, URL list, CAFFE website, MNIST Tutorial, CAFFE website, K-Means-IPython
8 06.06.17 Christian Schulze Similarity Search applications, prototypical architecture, distance measures, object search slides -
9 13.06.17 Damian Borth Visual Recognition and Image Classification 1 terminology, applications, face detection, face recognition, object recognition, visual words slides sheet, material
10 20.06.17 Damian Borth Visual Recognition and Image Classification 2 Bag-of-Words, Support Vector Machine (SVM), Spatial Location Extension, Concept Vocabularies slides -
11 27.06.17 Christian Schulze Video Analysis shot boundary detection, keyframe extraction, motion estimation & applications, audio analysis slides -
12 04.07.17 Marco Schreyer Deep Learning 1 - DL 101 and Autoencoders end-to-end learning, neural networks, autoencoder slides sheet, material
13 11.07.17 Damian Borth Deep Learning 2 Convolutional Neural Networks, Long-short Term Memory Networks, Generative Adversarial Networks slides -
14 18.07.17 Damian Borth Deep Learning 3 Visual Sentiment Analysis, AdjectiveNounPairs, DeepSentiBank slides Q&A Session

Last modified:: 24.10.2017