Subject: Data Mining (17.IISD17 )
Native organizations units: No data
Study programmes of the course:
| Type of studies | Title |
|---|---|
| Doctoral Academic Studies | Information Systems Engineering (Godina: 1, Summer) |
| Doctoral Academic Studies | Industrial Engineering / Engineering Management (Godina: 1, Summer) |
| Doctoral Academic Studies | Information Systems Engineering (Godina: 1, Summer) |
Educational goal:
The course is technology oriented and designed to provide an overview of the state-of-the-art in data mining and data science, as well as research training in these domains, to the doctoral students, who need to have basic knowledge of information technology, mathematics or a related field. Upon completion of the course the students will gain theoretical knowledge and practical skills, which will allow them to apply the technology in question to analyze large amounts of diverse data and embark on research projects in the area of data mining, data science, machine learning and artificial intelligence and their applications in their primary research areas.
Educational outcome:
Students will obtain the knowledge and skills that enable them to conduct independent scientific research in the domain of data mining and data science. They will obtain detailed insight of the state-of-the-art artificial intelligence and machine learning techniques used for data mining and in the domain of data science, their limitations and open research questions. Throughout the course they will be given a chance to take part in ongoing research projects, experiments and preparation of the results for publication. By the end of the course the students should have a draft of a scientific publication ready for submission to a relevant international scientific conference.
Course content:
The course will cover the following areas: review of main concepts of data mining, the typical sources and data preparation, decision trees, support vector machines, clustering of data, neural networks and deep learning, reinforcement learning, analysis and presentation of data that have temporal and spatial dimension. Theoretical instruction will be accompanied by research work training and students will take an active role in the research projects conducted at the faculty, design and conduct experiments, as well as prepare their results for publication.
Teaching methods:
Auditory and laboratory, supervised research work, seminar paper and oral examination.
Literature:
| Authors | Title | Year | Publisher | Language |
|---|---|---|---|---|
| 2009 | English | |||
| Ćulibrk, D. | Otkrivanje znanja iz podataka: Odabrana poglavlja | 2012 | Serbian language | |
| 2005 | English | |||
| 2007 | English | |||
| 2010 | English | |||
| 2008 | English |
Knowledge evaluation:
| Course activity | Pre-examination | Obligations | Number of points |
|---|---|---|---|
| Oral part of the exam | No | Yes | 30.00 |
| Project | Yes | Yes | 70.00 |
Lecturers:
prof. dr Ćulibrk Dubravko
Full Professor
Study research work
prof. dr Sladojević Srđan
Full Professor
Lectures
prof. dr Ćulibrk Dubravko
Full Professor
Lectures
prof. dr Mirković Milan
Full Professor
Lectures
prof. dr Mirković Milan
Full Professor
Study research work
prof. dr Sladojević Srđan
Full Professor
Study research work
Faculty of Technical Sciences
© 2024. Faculty of Technical Sciences.
Contact:
Address: Trg Dositeja Obradovića 6, 21102 Novi Sad
© 2024. Faculty of Technical Sciences.