Učni načrt predmeta

Predmet:
Sodobni IKT pristopi
Course:
Contemporary ICT Approaches
Študijski program in stopnja /
Study programme and level		 Študijska smer /
Study field		 Letnik /
Academic year		 Semester /
Semester
Informacijske in komunikacijske tehnologije, 2. stopnja vsi 1 1
Information and Communication Technologies, 2nd cycle all 1 1
Vrsta predmeta / Course type
Obvezni / Mandatory
Univerzitetna koda predmeta / University course code:
IKT3-910
Predavanja
Lectures		 Seminar
Seminar		 Vaje
Tutorial		 Klinične vaje
work		 Druge oblike
študija		 Samost. delo
Individ. work		 ECTS
30 30 240 10

*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:
doc. dr. Tome Eftimov
Sodelavci / Lecturers:
Jeziki / Languages:
Predavanja / Lectures:
Slovenščina, angleščina / Slovenian, English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisites:

Zaključen študijski program prve stopnje s področja naravoslovja, tehnike ali računalništva.

Student must complete first-cycle study programmes in natural sciences, technical
disciplines or computer science.

Vsebina:
Content (Syllabus outline):

Študenti se bodo seznanili z aktualnimi vsebinami študijskih področij študijskega programa druge stopnje informacijskih in komunikacijskih tehnologij (tehnologije znanja, inteligentni sistemi in robotika, komunikacijske tehnologije, računalniške strukture in sistemi, napredne internetne tehnologije, digitalna transformacija). Pregled izbranih IKT področij bo podan na sistematičen način, ki bo vključeval pregled razvoja področja, aktualne raziskovalne rezultate ter raziskovalne izzive.

Students will get an overview of the contemporary topics of the second-cycle study program of Information and Communication Technologies (knowledge technologies, intelligent systems and robotics, communication technologies, computer structures and systems, advanced Internet technologies, digital transformation). Review of the selected ICT areas will be presented in a systematic way, which will include a review of the development of the area, current research results and new
research challenges.

Temeljna literatura in viri / Readings:

Izbrani članki s področja obravnavanih vsebinskih področji informacijskih in komunikacijskih tehnologij (tehnologije znanja, inteligentni sistemi in robotika, komunikacijske tehnologije, računalniške strukture in sistemi, napredne internetne tehnologije, digitalna transformacija). / Selected articles in the field of information and communication technologies (knowledge technologies, intelligent systems and robotics, communication technologies, computer structures and systems, advanced Internet technologies, digital
transformation).

Cilji in kompetence:
Objectives and competences:

Cilj predmeta je pridobiti celostni pregled vsebin vseh modulov študijskega programa IKT2 z vidika njihovega dosedanjega razvoja, trenutnega stanja raziskav in njihovega bodočega razvoja.

Pomemben cilj je pridobiti poznavanje tematik celotnega študijskega programa ter s tem zagotoviti širino znanja, nujno potrebnega za pravilno umestitev konkretnega raziskovalnega dela študenta v širše raziskovalno področje ter uspešno povezovanje z drugimi raziskovalnimi
področji.

The aim of the course is to obtain a comprehensive overview of the content of all the modules of the ICT2 study program in terms of its development, research state of the art and its future development.

An important goal is to obtain a comprehensive understanding of the topics of the entire study program, thus ensuring broadness of the knowledge that is indispensable for placing the student's own research in the broader ICT research area and its successful integration with other research fields.

Predvideni študijski rezultati:
Intendeded learning outcomes:

Celosten pregled študijskega področja, razumevanje dosedanjega razvoja, trenutnega stanja in usmeritev bodočega razvoja področja. Študenti bodo tako pridobili široko znanje o IKT in sposobnost suverenega komuniciranja tako znotraj področja raziskav IKT kot tudi z drugimi
raziskovalnimi področji.

Comprehensive overview of the study field, understanding of its development, its state of the art and directions for future development of the field. Students will thus acquire broad knowledge of ICT and the ability of competent communication both within the field of ICT and with other research areas.

Metode poučevanja in učenja:
Learning and teaching methods:

Predavanja, konzultacije, druge metode

Lectures, consultations, other methods

Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Pisni izpit
100
Written exam
Reference nosilca / Lecturer's references:
1. • Kostovska, A., Vermetten, D., Korošec, P., Džeroski, S., Doerr, C., & Eftimov, T. (2024). Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks. Evolutionary Computation, 1-27.
2. • Cenikj, G., Petelin, G., & Eftimov, T. (2024). A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimization. Swarm and Evolutionary Computation, 87, 101534.
3. • Ispirova, G., Eftimov, T., Džeroski, S., & Seljak, B. K. (2024). MsGEN: Measuring generalization of nutrient value prediction across different recipe datasets. Expert Systems with Applications, 237, 121507.WALL, David P., DELGADO, Antonio, O'SULLIVAN, Lilian, CREAMER, Rachel, TRAJANOV, Aneta, KUZMANOVSKI, Vladimir, HENRICKSEN, Christian B., DEBELJAK, Marko. A decision support model for assessing the water regulation and purification potential of agricultural soils across Europe. Frontiers in sustainable food systems. [in press] 2020, 15 str. ISSN 2571-581X. DOI: 10.3389/fsufs.2020.00115
4. • Cenikj, G., Strojnik, L., Angelski, R., Ogrinc, N., Koroušić Seljak, B., & Eftimov, T. (2023). From language models to large-scale food and biomedical knowledge graphs. Scientific reports, 13(1), 7815.
5. • Eftimov, T., & Korošec, P. (2022). Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms. Springer Nature.