Artificial Intelligence for Data Analysis

AI-assisted data analysis is a process of discovering patterns and models, described by rules or other human- understandable representation formalisms. The most important step in this process is data mining, performed by using methods, techniques and tools for automated constructions of patterns and models from data.

The course objectives are to (a) introduce the basics of data mining, (b) outline the process of knowledge discovery in databases and the CRISP-DM methodology, (c) present the methodology for result evaluation, (d) present selected data mining methods and techniques by cases relevant for engineering and management, and (e) empower the students with the skills for practical use of selected data mining tools.

The students will master the basics of data preprocessing, data mining and knowledge discovery and will be capable of using selected data mining tools and results evaluation methods in practice.

Basic knowledge of mathematics, computer science and informatics is requested.

1. Introduction
2. Artificial Intelligence (AI) in a business environment
3. Data analysis following the CRISP-DM methodology
4. AI techniques for data analysis:
   - Analysis of tabular data
   - Heuristics for model and patterns construction
   - Quality of learned models and discovered patterns
   - Methodology for results evaluation
   - Text analysis
5. AI use cases in business, ecology, industry, etc.
6. Practical use of selected data analysis tools

Knowledge and understanding:

Mastering of selected Artificial Intelligence methods and techniques for data analysis, the capability of data preprocessing, practical use of selected data mining techniques, and capability of using and interpreting the methods for result evaluation.

Selected chapters from the following books:

• D. Mladenić, N. Lavrač, M. Bohanec, S. Moyle (eds.) Data Mining and Decision Support: Integration and Collaboration. Kluwer 2003. ISBN 1-4020-7388-7 Catalogue E-version
• J.H. Witten, E. Frank, M.A. Hall: Data Mining: Practical Machine Learning Tools and Techniques (Third Edition), Morgan Kaufmann, 2011. ISBN 978-0-12-374856-0 Katalog E-version
• M. Berthold (ed.), Bisociative Knowledge Discovery, Springer, 2012. ISBN 978-3-642-31829-0 Katalog E-version
• J. Fuernkranz, D. Gamberger, N. Lavrač: Foundations of Rule Learning. Springer, 2012. ISBN 978-3-540-75196-0 Catalogue E-version

Competence evaluation:
• By written exam we evaluate the basic knowledge of artificial intelligence for data analysis and the knowledge discovery process following the CRISP-DM methodology
• By seminar or project work and its oral defense we evaluate practical competencies of using the selected data analysis tools and methods for results evaluation
50/50

Pridr. prof. dr. Bojan Cestnik, rang redni profesor , hab. področje računalništvo,
je direktor podjetja za računalniški inženiring Temida in raziskovalec na odseku za Tehnologije znanja na Institutu Jožefa Stefana v Ljubljani. Iz računalniških znanosti je doktoriral leta 1991 na Fakulteti za računalništvo Univerze v Ljubljani. Strokovno se ukvarja s področji informacijskih sistemov, ki temeljijo na znanju, modeliranjem poslovnih sistemov, sistemi za podporo odločanju, in računalniško podprtim učenjem. Rezultate svojega raziskovalnega dela predstavlja v revijah in na mednarodnih konferencah. Ima izkušnje z vodenem razvoja in vzdrževanja mnogih velikih projektov programske opreme za podporo poslovanja.

Prof. dr. Bojan Cestnik, rang full professor, hab. Field computer scienc, is the general manager of software company Temida and a researcher in the department of Knowledge technologies at Jozef Stefan Institute in Ljubljana. He obtained his Ph.D. in Computer Science in 1991 at the Faculty of Electrical Engineering and Computer Science, University of Ljubljana, Slovenia. His professional and research interests include knowledge based information systems, business process modeling, decision support systems and machine learning. His research work was presented at several international conferences. He has been responsible for several large-scale software development and maintenance projects for supporting business operations.

Prof. dr. Aneta Ivanovska (prej Trajanov), izredna profesorica za področje računalništva in informatike na Univerzi v novi Gorici in direktorica študijskega programa Gospodarski inženiring 2. stopnje, je strokovnjakinja na področju umetne inteligence. Doktorirala je na področju strojnega učenja leta 2010 na Mednarodni Podiplomski Šoli Jožefa Stefana. Od leta 2005 do 2022 je delala kot raziskovalka na Odseku za Tehnologije Znanja na Inštitutu Jožef Stefan. Podoktorski študij je opravila na Inštitutu Ruđer Bošković v Zagrebu v letu 2015-2016. Njena glavna raziskovana področja so strojno učenje in odkrivanje znanja iz okoljskih podatkov, sistemi za podporo pri odločanju, induktivno logično programiranje in odkrivanje enačb. Sodelovala je v številnih Evropskih in nacionalnih projektih na področju agro-ekologije, kjer je uporabljala različne metode strojnega učenja za analizo (agro)ekoloških podatkov. Od novembra 2022 dela kot vodja oddelka za umetno inteligenco v podjetju MarineXchange, ki se ukvarja z razvojem programske opreme v potniški križarkarski industriji.

Prof. Dr. Aneta Trajanov (former Trajanov), Associate professor in the field of computer science and informatics at the University of Nova Gorica and a director of the Masters programme Management and Engineering, is an expert in the area of artificial intelligence. She completed her PhD on machine learning in 2010 at the Jozef Stefan International Postgraduate School. From 2005 until 2022 she was a researcher at the Department of Knowledge Technologies at the Jozef Stefan Institute. She completed her post-doc at the Ruđer Boškovič Institute, Zagreb, Croatia in 2015/2016. Her main research interests are machine learning and knowledge discovery from environmental data, decision support, inductive logic programming and equation discovery. She has worked on many European, as well as national, projects in the area of agroecology, where she applied different machine learning methods for analyzing (agro)ecological data. Since November 2022 she works as a Director of Artificial Intelligence in the company MarineXchange, which develops software for the cruise industry.

Izbrane objave / Selected bibliography

• CESTNIK, Bojan, FABBRETTI, Elsa, GUBIANI, Donatella, URBANČIČ, Tanja, LAVRAČ, Nada. Reducing the search space in literature-based discovery by exploring outlier documents : a case study in finding links between gut microbiome and Alzheimer's disease. Genomics and computational biology, ISSN 2365-7154, 2017, vol. 3, no. 3, str. e58-1-e58-10, doi: 10.18547/gcb.2017.vol3.iss3.e58. [COBISS.SI-ID 30497575]
• PEROVŠEK, Matic, KRANJC, Janez, ERJAVEC, Tomaž, CESTNIK, Bojan, LAVRAČ, Nada. TextFlows : a visual programming platform for text mining and natural language processing. Science of computer programming, ISSN 0167-6423, 2016, vol. 121, str. 128-152, doi: 10.1016/j.scico.2016.01.001. [COBISS.SI-ID 29549095]
• CESTNIK, Bojan, BOHANEC, Marko, URBANČIČ, Tanja. QTvity : advancing students' engagement during lectures by using mobile devices. V: RACHEV, Boris (ur.). CompSysTech'15 : proceedings of the 16th International Conference on Computer Systems and Technologies, June 25 - 26, 2015, Dublin, Ireland. New York: ACM. 2015, str. 334-341. http://dl.acm.org/citation.cfm?id=2812467&dl=ACM&coll=DL. [COBISS.SI-ID 29039143]
• CESTNIK, Bojan, URBANČIČ, Tanja. Teaching supply chain management with the beer distribution game on mobile devices. V: CABALLERO-GIL, Pino (ur.). Proceedings. [S. l.: s. n.]. 2014, str. 111-117. [COBISS.SI-ID 3560187]
• CESTNIK, Bojan, CHERNOGOROV, Fedor, KUKLIŃSKI, Slawomir, KRIŽMAN, Viljem. Framework for cognitive network implementation based on Cellar, Karaf, JADE and OSGi. V: BALANTIČ, Zvone (ur.), et al. Fokus 2020 : zbornik 33. mednarodne konference o razvoju organizacijskih znanosti = Focus 2020 : proceedings of the 33rd International Conference on Organizational Science Development. Kranj: Moderna organizacija. 2014, str. [1-8]. [COBISS.SI-ID 3009403]
• PETRIČ, Ingrid, CESTNIK, Bojan, LAVRAČ, Nada, URBANČIČ, Tanja. Outlier detection in cross-context link discovery for creative literature mining. The Computer journal, ISSN 0010-4620, 2012, vol. 55, no. 1, str. 47-61, doi: 10.1093/comjnl/bxq074. [COBISS.SI-ID 1621243]
• MACEDONI-LUKŠIČ, Marta, PETRIČ, Ingrid, CESTNIK, Bojan, URBANČIČ, Tanja. Developing a deeper understanding of autism : connecting knowledge through literature mining. autism res. treat., 2011, vol. 2011, 8 str. [COBISS.SI-ID 1916411]
• PUR, Aleksander, BOHANEC, Marko, LAVRAČ, Nada, CESTNIK, Bojan. Primary health-care network monitoring : a hierarchical resource allocation modeling approach. Int. j. health plann. manage., 2010, vol. 25, no. 2, str. 119-135. [COBISS.SI-ID 23721255]
• PETRIČ, Ingrid, URBANČIČ, Tanja, CESTNIK, Bojan, MACEDONI-LUKŠIČ, Marta. Literature mining method RaJoLink for uncovering relations between biomedical concepts. Journal of biomedical informatics, apr. 2009, vol. 42, no. 2, str. 219-227. [COBISS.SI-ID 929787]
• CESTNIK, Bojan, KERN, Alenka, MODRIJAN, Helena. Semi-automatic ontology construction for improving comprehension of legal documents. Lect. notes comput. sci., 2008, lNCS 5184, str. 328-339. [COBISS.SI-ID 23096103]
• PETRIČ, Ingrid, URBANČIČ, Tanja, CESTNIK, Bojan. Discovering hidden knowledge from biomedical literature. Informatica (Ljublj.), 2007, vol. 31, no. 1, str. 15-20, ilustr. [COBISS.SI-ID 634875]
• LAVRAČ, Nada, CESTNIK, Bojan, GAMBERGER, Dragan, FLACH, Peter A. Decision support through subgroup discovery : three case studies and the lessons learned. Mach. learn.. [Print ed.], 2004, vol. 57, str. 115-143. [COBISS.SI-ID 18515239]
• BOHANEC, Marko, CESTNIK, Bojan, RAJKOVIČ, Vladislav. Quasitative multi-attribute modeling and its application housting. Revue des systèmes de décision, 2001, vol. 10, str. 175-193. [COBISS.SI-ID 16555559]
• CESTNIK, Bojan, SUŠNIK, Janko, BIZJAK, Breda. Computerised estimation of the compatibility of stresses and strains at work. Informatica medica slovenica, 1996, letn. 3, št. 1,2,3, str. 101-108. [COBISS.SI-ID 7897049]