Universität Wien
Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.

052300 VU Foundations of Data Analysis (2022S)

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

The lectures of 02.03. and 03.03. will be digital.
Please find here the Zoom-link for the first lecture on March, 2:
https://univienna.zoom.us/j/63151141628?pwd=bmNTb3BFcnlUZ056YnVqMnVCUGdhQT09
Meeting-ID: 631 5114 1628
access code: 165159

Please find here the Zoom-link for the second lecture on March, 3:
https://univienna.zoom.us/j/65992634287?pwd=cTZlakEzK05LZ2J3cVc2alpQN2JEQT09
Meeting-ID: 659 9263 4287
access code: 163392

  • Mittwoch 02.03. 09:45 - 11:15 Digital
    Hörsaal II NIG Erdgeschoß
  • Donnerstag 03.03. 09:45 - 11:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Mittwoch 09.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 10.03. 09:45 - 11:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Mittwoch 16.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 17.03. 09:45 - 11:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Mittwoch 23.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 24.03. 09:45 - 11:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Mittwoch 30.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 31.03. 09:45 - 11:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Mittwoch 06.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 07.04. 09:45 - 11:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Mittwoch 27.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 28.04. 09:45 - 11:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Mittwoch 04.05. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 05.05. 09:45 - 11:15 Digital
  • Mittwoch 11.05. 09:45 - 11:15 Digital
  • Donnerstag 12.05. 09:45 - 11:15 Digital
  • Mittwoch 18.05. 09:45 - 11:15 Digital
  • Donnerstag 19.05. 09:45 - 11:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Mittwoch 25.05. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Mittwoch 01.06. 09:45 - 11:15 Digital
  • Donnerstag 02.06. 09:45 - 11:15 Digital
  • Mittwoch 08.06. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 09.06. 09:45 - 11:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Mittwoch 15.06. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Mittwoch 22.06. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 23.06. 09:45 - 11:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Mittwoch 29.06. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
  • Donnerstag 30.06. 16:30 - 18:00 Digital

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.

Due to the ongoing pandemic, we will adopt a mixed lecture format that complements pre-recorded video lectures with live (offline or online) review sessions. Details will be announced on Moodle.

Zoom-link to the first lecture on March, 2:
https://univienna.zoom.us/j/63151141628?pwd=bmNTb3BFcnlUZ056YnVqMnVCUGdhQT09
Meeting-ID: 631 5114 1628
access code: 165159

Zoom-link to the second lecture on March, 3:
https://univienna.zoom.us/j/65992634287?pwd=cTZlakEzK05LZ2J3cVc2alpQN2JEQT09
Meeting-ID: 659 9263 4287
access code: 163392

Art der Leistungskontrolle und erlaubte Hilfsmittel

- 2 labs (i.e. programming exercises including peer review), for each lab you will get a maximum of 18% of the required points.

- 2 pen-and-paper exercise sheets. They serve as a preparation for the exams. For each exercise sheet you will be able to get a maximum of 5% of the required points.

- 2 exams, one mid-term and one final, each 25% of the total points.

Furthermore you can complete:

- 1 exercise sheet to assess your current mathematical (prerequisite) knowledge, 1% of the total points.

- 3 anonymized feedbacks, each 1% of the total points.

- in addition you can earn at most 10% of bonus points for completing voluntary quizzes

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: Do 11.05.2023 11:27