Universität Wien

052300 VU Foundations of Data Analysis (2019S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung

http://vda.univie.ac.at/Teaching/FDA/
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Mittwoch 06.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 07.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Mittwoch 13.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 14.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Mittwoch 20.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 21.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Mittwoch 27.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 28.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Mittwoch 03.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 04.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Mittwoch 10.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 11.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 02.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Mittwoch 08.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Donnerstag 09.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Montag 13.05. 13:15 - 14:45 Seminarraum 4, Währinger Straße 29 1.UG
  • Mittwoch 15.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Donnerstag 16.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Mittwoch 22.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Donnerstag 23.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Mittwoch 29.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Mittwoch 05.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Donnerstag 06.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Mittwoch 12.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Donnerstag 13.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Mittwoch 19.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Mittwoch 26.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß
  • Donnerstag 27.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal II NIG Erdgeschoß

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Today's currency is data. However, data is only useful if we are able to extract useful information from it. This is the aim of data analysis in general. This course aims to survey the foundations of data analysis. This includes concepts from statistical inference, regression analysis, classification analysis, clustering analysis, dimensionality reduction.

Concepts as well as techniques are introduced and practiced.

Art der Leistungskontrolle und erlaubte Hilfsmittel

3 labs (i.e. programming exercises including peer review), for each lab you will get a maximum of 12% of the required points.
- 2 pen-and-paper exercise sheets. They serve as a preparation for the tests. For each exercise sheet you will be able to get a maximum of 5% of the required points.
- 2 exams, one mid-term where you can obtain up to 20% of the total points and one final with questions on the entire course where you can obtain up to 30%.
Furthermore you can complete:
- 1 exercise sheet to assess your current mathematical (prerequisite) knowledge.
- 3 anonymized feedbacks (for a maximum of 3 feedbacks i.e. 1% for each feedback) These feedbacks can either be returned to the Tutor responsible for the lecture in an anonymized manner.

Mindestanforderungen und Beurteilungsmaßstab

For bachelor students, the mandatory prerequisite for this class is the successful completion of the following courses:
- StEOP
- Programmierung 2 (PR2)
- Mathematische Grundlagen der Informatik 2 (MG2)
- Theoretische Informatik (THI)
- Modellierung (MOD)
- Algorithmen und Datenstrukturen (ADS)

Grading will be done according to the following scheme:
1 – at least 87.5%
2 – at least 75.0%
3 - at least 60.0%
4 – at least 40.0%

Please keep in mind that in order to pass the course, you will need at least 30% of the total score in all labs and homeworks combined with 40% of the total score of the tests.

In order to successfully pass the course, regular attendance is strongly recommended, however not mandatory.

Prüfungsstoff

1. Models, Statistical Inference, and General Techniques
1.1. Fundamental Concepts in Inference
1.2. Parametric Inference
1.3. Hypothesis Testing and p-values
1.4. The Bootstrap
1.5. Data Splitting, Cross-Validation
2. Regression Modelling
2.1. Simple Linear Regression
2.2. Multiple Regression
2.3. Further Regression Methods
2.4. Generalized Linear Models
2.5. Regression Trees
3. Classification Modelling
3.1. Decision Theoretic Introduction; Error rates, and Bayes Optimality
3.2. Logistic Regression
3.3. Classification Trees
3.4. Support Vector Machines
3.6. Further Classification Methods
4. Neural Networks
5. Basic Techniques of Unsupervised Learning
5.1. Dimension Reduction (Matrix Factorization)
5.2. Association Rules
6. Clustering Methods
6.1. Hierarchical Clustering
6.2. Model-based Clustering
6.3. Evaluation and Validation of Clustering Results
6.4. Density-based Clustering
6.5. Self Organizing Maps

Literatur

> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer 2007.
> Han, Kamber: Data Mining: Concepts and Techniques, Elsevier 2012.
> Hastie-Tibshirani-Friedman: The Elements of Statistical Learning, Springer 2009.
> James-Witten-Hastie-Tibshirani: An Introduction to Statistical Learning with Applications in R, Springer 2015.

Zuordnung im Vorlesungsverzeichnis

Module: FDA AKM SWI STW

Letzte Änderung: Mo 07.09.2020 15:30