| Statuss(Aktīvs) | Izdruka | Arhīvs(0) | Studiju plāns Vecais plāns | Kursu katalogs | Vēsture |
| Kurstitel | |
| Kurs-Code | InfTD019 |
| Kreditpunkte (ECTS) | 3 |
| Stundenzahl insgesamt (im Auditorium) | 81 |
| Vorlesungen (Stundenzahl) | 12 |
| Stundenzahl fŅr Seminare und praktische Arbeitsaufträge | 12 |
| Selbststandige Arbeit des Studenten (Stunden) | 57 |
| Bestätigt am (Datum) | 28/09/2022 |
| Kurs ausgearbeitet von (Lehrkraft) | |
| , Līga Paura , Irina Arhipova , Paolo Mignone , Domenico Redavid , Gatis Vītols |
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| 6979@@Priekšzināšanas Kursam priekšzināšanas nav nepieciešamas |
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| Zur einfŅhrenden LektŅre empfohlen | |
| 1.Ethem Alpaydin. Introduction to Machine Learning. Fourth Edition: The MIT Press, 2020. - 659 p.
2.Laura Igual, Santi Seguí. Introduction to Data Science. A Python Approach to Concepts, Techniques and Applications. – Switzerland: Springer International Publishing, 2017. - 217 p. 3.Oded Maimon, Lior Rokach. Data Mining and Knowledge Discovery Handbook. Springer New York, NY, 2010. - 1285 p. |
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| Weiterfuhrende Literatur | |
| 1.Ian H. Witten, Eibe Frank and Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques. - Elsevier Science, 2011. – 1190 p.
2.Data science & big data analytics: discovering, analyzing, visualizing and presenting data / EMC Education Services. - Indianapolis, IN: John Wiley and Sons, 2015. - xviii, 410 p. 3.Weka Wiki: The University of Waikato. Pieejams: https://waikato.github.io/weka-wiki/documentation/ |
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| Zur LektŅre vorgeschlagene Zeitschriften | |
| 1.Journal of Data Analysis and Information Processing: ISSN Online: 2327-7203, www.scirp.org/journal/jdaip | |