136009 AR Applied Data Science for Linguists (2023S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 06.02.2023 08:00 bis Mo 27.02.2023 08:00
- Abmeldung bis Fr 31.03.2023 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Montag 20.03. 11:30 - 13:00 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
- Montag 27.03. 11:30 - 16:00 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
- Montag 17.04. 11:30 - 16:00 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
- Montag 08.05. 11:30 - 16:00 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
- Montag 05.06. 11:30 - 16:00 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Pre-course exercise (to be handed in online), four home assignments (R and RStudio on your own computer), and participation in class
Mindestanforderungen und Beurteilungsmaßstab
The ability to analyze linguistic data as well as the ability to understand and interpret statistical analyses, to fit statistical models and to use these models for making predictions. The ability to use R and RStudio for this purpose.Assessment:
Pre-course exercise: 5%
Four home assignments: 20% each
Participation in class: 15%
Minimum pass grade: 60% in total
Pre-course exercise: 5%
Four home assignments: 20% each
Participation in class: 15%
Minimum pass grade: 60% in total
Prüfungsstoff
Literatur
Baayen, R. H. (2008) Analyzing linguistic data: A practical introduction to statistics using R. Cambridge: Cambridge University Press.
Butler, C. (1985). Statistics in linguistics. Oxford: Blackwell.
Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745-766.
Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266.
Feinerer, I. (2018). Introduction to the tm Package Text Mining in R. http://cran.uib.no/web/packages/tm/vignettes/tm.pdf
Butler, C. (1985). Statistics in linguistics. Oxford: Blackwell.
Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745-766.
Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266.
Feinerer, I. (2018). Introduction to the tm Package Text Mining in R. http://cran.uib.no/web/packages/tm/vignettes/tm.pdf
Zuordnung im Vorlesungsverzeichnis
EC DH 2
Letzte Änderung: Do 04.07.2024 00:13
In this course, we will make use of the scripting language R together with its frontend RStudio. Both are pre-installed on the computers in the lab, but you might want to install them on your own computers as well (e.g. for doing the exercises at home). You will learn how to use R as we go along. Further instructions and literature will be provided on Moodle.This is an introductory course. As such, no previous knowledge of statistics, statistical software, machine learning or programming is required, but a solid knowledge of high school mathematics (at least Unterstufe) will prove useful (linear functions, basic arithmetic operations, fractions, percentages, probability etc.). Since this course is aimed at a linguistically trained audience, I will take knowledge of fundamental linguistic concepts for granted.