MS1PSZ - Data Mining and Semantic Web

Course specification
Course title Data Mining and Semantic Web
Acronym MS1PSZ
Study programme Electrical Engineering and Computing
Module Computer Engineering and Informatics
Type of study master academic studies
Lecturer (for classes)
    Lecturer/Associate (for practice)
      Lecturer/Associate (for OTC)
        ESPB 6.0 Status elective
        Condition Databases 1, Expert systems
        The goal Introduce students to the fundamental concepts and principles of data mining, machine learning, semantic web technologies, and concept modeling. Introduce students to the principles of design and implementation of data mining models and semantic web ontologies.
        The outcome Students will be able to understand how knowledge and data can be conceptualized, organized, searched, stored, and retrieved. They will be equipped with knowledge concerning machine learning, data mining, semantic web technologies and concept modeling.
        Contents of lectures Data mining and Knowledge Mining , Semantic Web and Concept Web, Concept Modeling
        Contents of exercises Same as for the theoretical lessons. Examples of specific algorithms and tools, including Protege and Microsoft SQL Server: Integration and Analysis Services.
        1. Jiawei Han, Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, USA (Original title)
        2. H. Peter Alesso, Craig F. Smith, Developing Semantic Web Service, A K Peters, USA (Original title)
        3. Larose D. , Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, 2005 (Original title)
        4. Antoniou G., van Harmelen F. , A Semantic Web Primer, Second Edition, Cooperative Information Systems, The MIT Press, 2009 (Original title)
        5. Najnoviji radovi po izboru predavača (Original title)
        Number of hours per week during the semester/trimester/year
        Lectures Exercises OTC Study and Research Other classes
        2 2
        Methods of teaching Lectures, demonstrations, exercises, projects.
        Knowledge score (maximum points 100)
        Pre obligations Points Final exam Points
        Activites during lectures 0 Test paper 20
        Practical lessons 0 Oral examination 20
        Projects 60
        Colloquia 0
        Seminars 0