Učni načrt predmeta

Predmet:
Računalniško podprto odkrivanje znanstvenih zakonitosti iz strukturiranih, prostorskih in časovnih podatkov
Course:
Computational Scientific Discovery from Structured, Spatial and Temporal Data
Študijski program in stopnja /
Study programme and level
Študijska smer /
Study field
Letnik /
Academic year
Semester /
Semester
Informacijske in komunikacijske tehnologije, 3. stopnja Tehnologije znanja 1 1
Information and Communication Technologies, 3rd cycle Knowledge Technologies 1 1
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT3-723
Predavanja
Lectures
Seminar
Seminar
Vaje
Tutorial
Klinične vaje
work
Druge oblike
študija
Samost. delo
Individ. work
ECTS
15 15 15 105 5

*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. Sašo Džeroski
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:
Seminarska naloga
50 %
Seminar work
Ustni zagovor seminarske naloge
50 %
Oral defense of seminar work
Reference nosilca / Lecturer's references:
1. J. Levatić, D. Kocev, M. Ceci, S. Džeroski. Semi-supervised trees for multi-target regression. Information Sciences 450: 109-127, 2018.
2. A. Osojnik, P. Panov, S. Džeroski. Multi-label classification via multi-target regression on data streams. Machine Learning 106 (6), 745-770, 2017.
3. J. Levatić, D. Kocev, S. Džeroski. The importance of the label hierarchy in hierarchical multi-label classification. Journal of Intelligent Information Systems. 45 (2), 247-271, 2015.
4. P. Panov, L. Soldatova, and S. Džeroski. Ontology of core data mining entities. Data Mining and Knowledge Discovery 28 (5-6), 1222-1265, 2014.
5. D. Kocev, C. Vens, J. Struyf, and S. Džeroski. Tree ensembles for predicting structured outputs. Pattern Recognition 46 (3), 817-833, 2013.