|Application deadline:||June 15th|
|Tuition fee:|| |
|Start date:||September 2015, October 2015|
|Duration full-time:||24 months|
|Partnership:|| Erasmus Mundus Scholarship |
|Delivery mode:||On Campus|
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The sheer amount of numerical data, textual documents, images, video, Web sites available today is overwhelming, and cannot satisfy, per se, the emerging knowledge society. It is indeed necessary to extract, from this wealth of information, the knowledge hidden inside. Only this ability could guarantee a better future to the individuals and the society, as well as a sustainable economical development and competitiveness.
To locate useful information, to transform it into actionable knowledge, and to manage its use for decision-making can be accomplished through the exploitation of methodologies and tools of Data Mining and Knowledge Management (DMKM). Notwithstanding the availability on the market and in academic environments of advanced solutions and systems, DMKM still calls for further research and developments to face new important challenges. In particular, some hot issues are still to be tackled, such as the following ones:
* To face the exponential increase of data it is not sufficient to rely on larger storing devices and/or faster computers. New intelligent approaches are to be designed to tame the very size of data.
* Data assume different modalities, such as numbers, texts, audio-video, sensor signals, and so on. Integrating into a unique system such complex data is still a challenge. Also, spatial distribution of data (for instance, on several Web sites or different data bases) is a source of difficulty for integration.
More importantly, there is still a "semantic gap" between the form in which the data are represented and used by a computer and their "meaning" for a human user. The new emerging techniques for the Semantic Web are trying to close this gap.
Beyond the economic, scientific and technological challenges, the reasons that have motivated the members of the consortium to propose this Erasmus Mundus Master's degree are based on multiple observations:
At the end of the programme, the successful students shall have acquired competences in computer science, applied mathematics and statistics, and advanced information processing methods that will allow them to: * Perform in depth analysis of information requirements for solving problems;
* Manage large databases;
* Make use of such databases with the goal of extracting from them hidden information and knowledge;
* Deploy this knowledge in decision support systems or intelligent systems, both in academic and in industrial environments.
* Around 50% of the programme is devoted to applications in the field, solving real data mining problems and the implementation of the standard techniques liable to be encountered by students in their future professional activities.
Admission criteria: To be eligible to apply for the Master programme DMKM, students must satisfy both the following conditions:
* Be the holder of a Bachelor degree (a minimum of three years' study at a university and corresponding to the equivalent of 180 ECTS) in the fields of Computer Science, Mathematics or Statistics.
* If the above list does not mention your test, please upload your certificate of proficiency in English in the application online.
Academic evaluation criteria of the candidate's curriculum: Evaluation of the academic curriculum of the candidate is based on the following criteria:
* Quality of the results obtained in the last three years of university study (20 pts);
* Adequacy of knowledge already acquired in the Masters subjects (10 pts);
* Previous work placements (2 pts);
* Knowledge of foreign languages, excluding English and the mother language (2 pts);
* Evaluations, made by at least two teachers (or senior researchers) who have known the student, regarding his/her suitability to enroll in a Master programme (5 pts);
* Student's motivations (20 pts).
Communication evaluation criteria of the candidate interview: Evaluation of the interview is based on the following criteria:
* Understanding of the English language (4 pts);
* Ability to speak English (4 pts);
* Candidate's motivations to enroll in the Master (8 pts);
* Consistency between the candidate's professional expectations and the goal of the Master (6 pts);
* Match between the candidate's previous knowledge and the content of the Master courses (4 pts);
|TOEFL paper-based test score :||550|
|TOEFL iBT® test:||79|
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