13M111PSZ - Data Mining and Semantic Web
Course specification | ||||
---|---|---|---|---|
Course title | Data Mining and Semantic Web | |||
Acronym | 13M111PSZ | |||
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 | ||||
URL to the subject page | http://rti.etf.bg.edu.rs/rti/ms1psz/ | |||
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. | |||
Literature | ||||
| ||||
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 | ||||
Colloquia | 0 | |||
Seminars | 60 |