Checking date: 17/05/2019


Course: 2019/2020

Multimedia information management
(8941)
Study: Master in Advanced Communications Technologies (278)
EPI


Coordinating teacher: PELAEZ MORENO, CARMEN

Department assigned to the subject: Department of Signal and Communications Theory

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Competences and skills that will be acquired and learning results.
- Descriptive knowledge about the information overload problem, the differences between information and contents and the flows of content. - Skills to use indexing techniques in text, audio, speech, image and video. - Skills to model Information Retrieval and Filtering techniques.
Description of contents: programme
The modern information overload problem caused by the availability of enormous amounts of information through internet makes it necessary to design systems that allow us to find the information we search and filter or personalize the information according to our needs. For that matter it is fundamental to be able to automatically index not only textual contents but also audio (music, speech, etc.), image or video, using methods based on the content or even collaborative tagging as the one taking place in social networks. Examples of these multimedia management systems are Google search (and all its variants as Google Image, Google Goggles, etc.), recommender systems and user profilers like those available in Amazon, etc. Topic 1. Introduction to text processing for information retrieval Topic 2. Feature extraction techniques for speech, audio, image and video indexing Topic 3. Modeling of information retrieval tasks Topic 4. Recommender systems, user profiling and content filtering
Learning activities and methodology
The following learning activities and methodologies are combined: - Theory classes - Guided lab assignments - Research papers' preseentations - Final project Teachers are available during 2 hours per week for office hours.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • C. D. Manning, P. Raghavan and H. Schültze. Introduction to Information Retrieval. MIT press. 2008
  • G. G. Chowdury. Introduction to Modern Information Retrieval, 3nd ed.. Neal-Schuman Publishers. 2010
  • M. Lew. Principles of Visual Information Retrieval. Springer. 2001
  • Pamela Forner, Henning Mu¿ller, Roberto Paredes. Information Access Evaluation: multilinguality, multimodality and visualization. Springer. 2013
  • R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval, 2nd ed.. Addison-Wesley. 2011
Additional Bibliography
  • A. Hanjalic. Content-based Analysis of Digital Video. Kluwer Academic Publishers. 2004
  • Anand Rajaraman, Jeffrey D. Ullman. Mining of Massive Datasets. Cambridge. 2014
  • Bing Liu. Web Data Mining: exploring hyperlinks, contents and usage data. Springer. 2011
  • C. D. Manning and H. Schu¿tze. Foundations of statistical natural language processing. MIT Press. 1999
  • C. J. van Rijsbergen. The Geometry of Information Retrieval. Cambridge University Press. 2004
  • Claudio Carpineto, Giovanni Romano. Concept Data Analysis: theory and applications. Willey. 2004
  • Daniel Jurafsky, James H. Martin. Speech and Language Processing, 2nd Edition. Prentice Hall. 2008

The course syllabus and the academic weekly planning may change due academic events or other reasons.