TU Wien:Statistische Simulation und computerintensive Methoden VU (Posekany)

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Daten[edit]

Lecturers Alexandra Posekany
ECTS 3
Alias Statistical Simulation and Computerintensive Methods (en)
When winter semester
Last iteration 2021WS
Language Deutsch
Mattermost statistische-simulation-und-computerintensive-methoden0RegisterMattermost-Infos
Links tiss:107330
Zuordnungen
E033531 Wahlmodul Wahl-LV-Katalog "Data Engineering & Statistics"
Bachelor Medizinische Informatik Wahlmodul Multivariate und computerintensive statistische Methoden
Bachelor Software & Information Engineering Wahlmodul Multivariate und computerintensive statistische Methoden
Master Data Science Wahlmodul MLS/CO - Machine Learning and Statistics - Core


Inhalt[edit]

noch offen, bitte nicht von tiss oder Homepage kopieren, sondern aus Studierendensicht beschreiben.

Ablauf[edit]

2021WS: Lectures are pre-recorded and provided in Tuwel. There are 10 Exercises. Out of 10 Exercises 9 will be mandatory which means that you can either miss one due to sickness or any other reason or that the worst result will be taken out of your grading, if you handed in 10 home works. For a positive grade you should achieve at least 3 points on 5 out of the 9 best exercises.

Benötigte/Empfehlenswerte Vorkenntnisse[edit]

noch offen

Vortrag[edit]

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Übungen[edit]

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Prüfung, Benotung[edit]

noch offen

Dauer der Zeugnisausstellung[edit]

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Zeitaufwand[edit]

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Unterlagen[edit]

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Tipps[edit]

noch offen

Verbesserungsvorschläge / Kritik[edit]

The whole lecture is a joke: Ms Prosekany seems to not really be interested if the students learn anything and therefore puts in minimal effort. The slides are directly copied from the predecessor where she just set her name instead of the other ones (copyright? at least state the name of the original creator...). The slides and videos are as informative as if you would watch a politician talk: much talking without transferring any information. This was also the case last semester and is the same for this semester, so there seems to be no light at the end of the tunnel. This lecture is an insult to the quality of the data science master and should therefore be completely revamped or removed.

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