Učni načrt predmeta

Predmet:

Umetna inteligenca in znanost

Course:

Artificial Intelligence for Science

Študijski program in stopnja / Study programme and level	Študijska smer / Study field	Letnik / Academic year	Semester / Semester
Nanoznanosti in nanotehnologije, Informacijske in komunikacijske tehnologije, Ekotehnologije, Senzorske tehnologije, 3. stopnja	/	1	1
Nanosciences and Nanotechnologies, Information and Communication Technologies, Ecotechnologies, Sensor Technologies, 3rd cycle	/	1	1

Vrsta predmeta / Course type

Izbirni / Elective

Univerzitetna koda predmeta / University course code:

SPL-906

Predavanja Lectures	Seminar Seminar	Vaje Tutorial	Klinične vaje work	Druge oblike študija	Samost. delo Individ. work	ECTS
20		10		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:

doc. dr. Panče Panov , prof. dr. Ljupčo Todorovski

Jeziki / Languages:

Predavanja / Lectures:

slovenščina, angleščina / Slovenian, English

Vaje / Tutorial:

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

Prerequisites:

Izpolnjeni morajo biti pogoji za vpis na doktorski študij: zaključena druga stopnja bolonjskega študija ali diploma univerzitetnega študijskega programa. Potrebna so tudi osnovna znanja računalništva oz. informatike.

Students must fulfill the formal requirements for enrolling to the doctoral study program: completed Bologna second level study program or an equivalent pre-Bologna university study program. Basic knowledge of computer science or informatics is also required.

Vsebina:

Content (Syllabus outline):

Umetna inteligenca (UI) in znanost: Uvod
Osnovni gradniki znanja v znanosti
Principi avtomatizirane znanosti in računalniškega
odkrivanja znanstvenih zakonitosti
Strojno učenje in napovedno modeliranje
Odprta znanost

Formalizmi za predstavitev znanja v znanosti
Od podatkov, preko modelov, do teorij
Znanstvene taksonomije in ontologije
Metode sklepanja za ontologije

Principi odprte znanosti
Reproducibilnost znanstvenih poskusov
Večkratna uporaba raziskovalnih rezultatov
Principi FAIR (najdljivost, dostopnost,
interoperabilnost, večkratna uporabnost)
za znanstvene podatke in modele
Meta-podatki in ontologije za znanost

Strojno učenje za analizo znanstvenih podatkov
Simbolične metode strojnega učenja
Obravnava strukturiranih podatkov
Umetne nevronske mreže in globoko učenje
Obravnava nestrukturiranih in
visokodimenzionalnih podatkov

Primeri uporabe: Študije primerov uporabe metod umetne inteligence in strojnega učenja v znanosti
Uporaba metod UI v fiziki
Primeri iz fizike delcev
Primeri iz znanosti o materialih
Uporaba metod UI v znanostih o življenju
Napovedovanje funkcij genov
Analiza mikrobiomskih podatkov
Virtualno presejalno testiranje spojin

Introduction: Artificial Intelligence (AI) for science
The basic components of scientific knowledge
Principles of automated science and computational scientific discovery
Machine learning (ML) and predictive modeling
Open science

Formal representation of scientific knowledge
From data through models to theories
Scientific taxonomies and ontologies
Reasoning methods for ontologies

Principles of open science
Reproducibility of scientific experiments
Reusability of research outputs
FAIR (findable, accessible, interoperable and
reusable) principles for scientific data and models
Meta-data and ontologies for science

Machine learning for the analysis of scientific data
Symbolic machine learning methods
Handling structured and semi-structured data
Artificial neural networks and deep learning
Handling unstructured and high-dimensional data

Applications: Case studies of using artificial intelligence and machine learning in science
Applications of AI in physics
Particle physics
Materials science
Applications of AI in life sciences
Gene function prediction
Analzying microbiome data
Virtual compound screening

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:
• Džeroski S., and Todorovski L., editors. Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences. Springer, 2007. ISBN 978-3-540-73919-7.
• S. Džeroski, B. Goethals, and P. Panov, Eds. Inductive Databases and Constraint-Based Data Mining. Springer, 2010. ISBN 978-1-4419-7737-3
• James G., Witten D., Hastie T., and Tibshirani R. An Introduction to Statistical Learning. Springer, 2013. ISBN 978-1-4614-7138-7.
• Arp R., Smith B., and Spear A.D. Building Ontologies with the Basic Formal Ontology. MIT Press, 2015.
• Goodfellow I., Bengio, Y., and Courville, A. Deep learning. MIT Press, 2016. ISBN 978-0262035613.

Cilji in kompetence:

Objectives and competences:

Cilj predmeta je seznaniti študenta s področji umetne inteligence in strojnega učenja, vključno z osnovnimi koncepti in sodobnimi metodami, s poudarkom na njihovi uporabi v znanosti.

Kompetence študenta z uspešno zaključenim predmetom bodo vključevale razumevanje osnovnih pojmov iz področja UI in strojnega učenja, poznavanje sodobnih metod in sposobnost samostojne uporabe teh metod pri reševanju znanstvenih problemov v skladu s principi odprte znanosti.

The course objective is to familiarize the student with the fields of artificial intelligence and machine learning, including basic concepts and state of the art methods, with a focus on their applications in science.

The competencies of the students successfully completing this course will include the understanding of basic concepts from the field, familiarity with the state-of-the art methods, capability of independent use of AI and ML methods for solving scientific problems while following the principles of open science.

Predvideni študijski rezultati:

Intendeded learning outcomes:

• Dobiti pregled obstoječih nalog in metod v umetni inteligenci in strojnem učenju, kot tudi študij primerov njihove uporabe na področju fizike in znanostih o življenju
• Pridobiti sposobnost formulacije konkretnih problemov iz posameznega izbranega znanstvenega področja kot problemov strojnega učenja
• Pridobiti sposobnost ugotavljanja primernosti različnih metodoloških pristopov za reševanje posameznih problemov s strojnim učenjem
• Pridobiti sposobnost slediti splošnim principom odprte znanosti pri lastnem raziskovalnem delu

• Acquiring an overview of existing tasks and methods in artificial intelligence and machine learning and case studies of their use in physics and life sciences
• Obtaining the ability to formulate problems specific to a selected scientific discipline as machine learning problems
• Obtaining the ability to identify the best methodological approach available for solving specific problems with machine learning
• Obtaining the ability to follow the general principles of reproducible science

Metode poučevanja in učenja:

Learning and teaching methods:

Predavanja, vaje, konzultacije, samostojno delo

Lectures, excercises, consultancy, individual work

Načini ocenjevanja:

Delež v % / Weight in %

Assesment:

Ustni izpit

Oral Exam

Seminarska naloga

Seminar Work

Ustni zagovor seminarske naloge

Oral Defense of the Seminar Work

Reference nosilca / Lecturer's references:

1.	N Omejc, B Gec, J Brence, L Todorovski, S Džeroski (2024) Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data. Machine Learning, DOI: 10.1007/s10994-024-06522-1
2.	J Brence, J Tanevski, J Adams, E Malina, S Džeroski (2023) Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations. Machine Learning 112, 1337-1363, DOI: 10.1007/s10994-022-06155-2
3.	B. Roy, T Stepišnik, C Vens, S Džeroski (2022) Survival analysis with semi-supervised predictive clustering trees. Computers in Biology and Medicine, 141, 105001, DOI: 10.1016/j.compbiomed.2021.105001
4.	J Brence, L Todorovski, S Džeroski (2021) Probabilistic grammars for equation discovery. Knowledge-based systems, 224, 107077, DOI: 10.1016/j.knosys.2021.107077
5.	J Levatić, M Ceci, T Stepišnik, S Džeroski, D Kocev (2020) Semi-supervised regression trees with application to QSAR modelling. Expert Systems with Applications 158, 113569, DOI: 10.1016/j.eswa.2020.113569