Učni načrt predmeta

Predmet:

Strojno učenje

Course:

Machine Learning

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:

IKT3-915

Predavanja Lectures	Seminar Seminar	Vaje Tutorial	Klinične vaje work	Druge oblike študija	Samost. delo Individ. work	ECTS
30	30				210	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:

prof. dr. Nada Lavrač

Sodelavci / Lecturers:

dr. Aljaž Osojnik , doc. dr. Bernard Ženko , doc. dr. Martin Žnidaršič

Jeziki / Languages:

Predavanja / Lectures:

Slovenščina, angleščina / Slovenian, English

Vaje / Tutorial:

Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:

Prerequisites:

Zaključen študij druge stopnje s področja informacijskih ali komunikacijskih tehnologij ali zaključen študij druge stopnje na drugih področjih z znanjem osnov s področja predmeta. Potrebna so tudi osnovna znanja matematike, računalništva in informatike.

Completed second cycle studies in information or communication technologies or completed second cycle studies in other fields with knowledge of fundamentals in the field of this course. Basic knowledge of mathematics, computer science and informatics is also requested.

Vsebina:

Content (Syllabus outline):

• Uvod v strojno učenje: osnovne in napredne naloge strojnega učenja, CRISP-DM metodologija odkrivanja zakonitosti.
• Tehnike strojnega učenja: Naivni Bayesov klasifikator, učenje odločitvenih, regresijskih in modelnih dreves, učenje klasifikacijskih in povezovalnih pravil, odkrivanje podskupin, relacijsko učenje, razvrščanje v skupine,
metoda najbližjih sosedov, napovedno razvrščanje, delno nadzorovano učenje, učenje predstavitev, logistična regresija, metoda podpornih vektorjev, umetne nevronske mreže, ansambli klasifikatorjev, aktivno učenje, razumljiva umetna inteligenca, globoko učenje.
• Hevristike in ocenjevanje rezultatov: preiskovalne hevristike, mere za ocenjevanje kvalitete naučenih vzorcev in modelov, metodologija vrednotenja rezultatov.
• Praktično usposabljanje: praktična uporaba izbranih tehnik in orodij strojnega učenja.

• Introduction to machine learning: elementary and advanced machine learning tasks, CRISP-DM knowledge discovery methodology.
• Machine learning techniques: Naive Bayesian classifier, decision, regression and model tree learning, learning classification and association
rules, subgroup discovery, relational learning, clustering, nearest neighbors approach, predictive clustering, semi-supervised learning,
representation learning, logistic regression, support vector machines, artificial neural networks, ensemble classifiers, active learning,
explainable artificial intelligence, deep learning.
• Heuristics and ML results evaluation: search heuristics, measures for estimating the quality of induced patterns and models, methodology for
results evaluation.
• Practical training: practical use of selected machine learning techniques and tools.

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:
• G. James, D. Witten, T. Hastie, and R. Tibshirani (1st Edition 2013, 2nd Edition 2021) An Introduction to Statistical Learning: with Applications in R. Springer, New York (available at https://statlearning.com/), ISBN-10: 1071614177, ISBN-13: 978-1071614174.
• J. Fürnkranz, D. Gamberger, and N. Lavrač, Foundations of Rule Learning. Springer, 2012. ISBN 978-3-540-75196-0.
• C.C. Aggarwal. Data mining: The textbook, Springer, 2015. ISBN 978-3-319-14141-1.
• N. Lavrač N, V. Podpečan, and M. Robnik-Šikonja (2021) Representation Learning: Propositionalization and Embeddings. Springer, Berlin. ISBN: 978-3-030-68817-2.
• J.H. Witten, E. Frank, and M.A. Hall (2011) Data Mining: Practical Machine Learning Tools and Techniques (3rd Edition). Morgan Kaufmann, ISBN 978-0-12-374856-0.
• S. Russell, P. Norvig (2010) Artificial Intelligence: A Modern Approach (3rd Edition), Prentice Hall, ISBN-10: 0136042597, ISBN-13: 978-0136042594.

Cilji in kompetence:

Objectives and competences:

Strojno učenje je najpomembnejše področje umetne inteligence, katerega cilj je avtomatska konstrukcija klasifikacijskih modelov in odkrivanje vzorcev v podatkih.

Cilji predmeta so predstaviti osnovne in napredne tehnike strojnega učenja ter metodologijo za ocenjevanja rezultatov strojnega učenja.

Študenti bodo obvladali osnovne in napredne tehnike strojnega učenja in bodo usposobljeni za praktično uporabo izbranih orodij strojnega učenja
in metod za evalvacijo rezultatov.

Machine learning is the most important area of artificial intelligence, whose goal is automated construction of classification models and pattern
discovery from data.

The course objectives are to present the basic and advanced machine learning techniques, and the methodology for machine learning results
evaluation.

The students will master the basic and advanced machine learning techniques, and will be capable of using selected machine learning tools and results evaluation methods in practice.

Predvideni študijski rezultati:

Intendeded learning outcomes:

Obvladana uporaba izbranih metod in tehnik strojnega učenja, usposobljenost za praktično uporabo izbranih orodij strojnega učenja, usposobljenost za uporabo in interpretacijo metod za evalvacijo rezultatov.

Mastering of selected machine learning methods and techniques, the capability of practical use of selected machine learning techniques, and the
capability of using and interpreting the methods for result evaluation.

Metode poučevanja in učenja:

Learning and teaching methods:

Predavanja, seminar, konzultacije, individualno delo

Lectures, seminar, consultancy, individual work

Načini ocenjevanja:

Delež v % / Weight in %

Assesment:

Pisni izpit

Written exam

Seminarska naloga

Seminar work

Ustni zagovor seminarske naloge

Oral defense of the seminar work

Reference nosilca / Lecturer's references:

1.	J. Fürnkranz, D. Gamberger, and N. Lavrač, Foundations of Rule Learning. Springer, 2012.
2.	M. Grau Leguia, Z. Levnajić, L. Todorovski, B. Ženko. Reconstructing dynamical networks via feature ranking. Chaos, 2019, vol. 29, no. 9, pp. 09310-1-093107-15.
3.	I. Ravkic, M. Žnidaršič, J. Ramon, J. Davis. Graph sampling with applications to estimating the number of pattern embeddings and the parameters of a statistical relational model. Data mining and knowledge discovery. vol. 32, no. 4, pp. 913-948.
4.	M. Grčar, N. Trdin, and N. Lavrač. A methodology for mining document-enriched heterogeneous information networks. The Computer Journal, 56(3): 321-335, 2013.
5.	N. Lavrač, V. Podpečan, and M. Robnik-Šikonja: Representation Learning: Propositionalization and Embeddings, Springer, 2021.