Učni načrt predmeta

Predmet:
Podatkovno in tekstovno rudarjenje
Course:
Data and Text Mining
Študijski program in stopnja /
Study programme and level
Študijska smer /
Study field
Letnik /
Academic year
Semester /
Semester
Informacijske in komunikacijske tehnologije, 2. stopnja Tehnologije znanja 1 1
Information and Communication Technologies, 2nd cycle Knowledge Technologies 1 1
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT2-713
Predavanja
Lectures
Seminar
Seminar
Vaje
Tutorial
Klinične vaje
work
Druge oblike
študija
Samost. delo
Individ. work
ECTS
60 30 60 450 20

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Nada Lavrač
Sodelavci / Lecturers:
Jeziki / Languages:
Predavanja / Lectures:
slovenščina, angleščina / Slovenian, English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisits:
Vsebina:
Content (Syllabus outline):
Temeljna literatura in viri / Readings:
Cilji in kompetence:
Objectives and competences:
Predvideni študijski rezultati:
Intendeded learning outcomes:
Metode poučevanja in učenja:
Learning and teaching methods:
Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Seminar
50 %
Seminar
(Pisni ali ustni) izpit
50 %
(Written or oral) exam
Reference nosilca / Lecturer's references:
1. J. Fürnkranz, D. Gamberger, and N. Lavrač, Foundations of Rule Learning. Springer 2012.
2. B. Sluban, D. Gamberger, and N. Lavrač, Ensemble-based noise detection: noise ranking and visual performance evaluation. Data Min. Knowl. Discov. 28(2): 265-303, 2014.
3. A. Vavpetič, V. Podpečan, and N. Lavrač, Semantic subgroup explanations. J. Intell. Inf. Syst. 42(2): 233-254, 2014.
4. J. Brank, D. Mladenić, M. Grobelnik. Feature construction in text mining. In: C. Sammut,G. Webb Eds. Encyclopedia of machine learning and data mining. Heidelberg [etc.]: Springer. 2016.
5. I. Petrič, and B. Cestnik, Predicting future discoveries from current scientific literature. In: KUMAR, Vinod D. (ur.). Biomedical Literature Mining, Methods in Molecular Biology, ISSN 1064-3745, vol. 1159). New York [etc.]: Humana Press, cop. 2014, pp. 159-168.