Universität Wien FIND

052300 VU Foundations of Data Analysis (2018W)

Prüfungsimmanente Lehrveranstaltung

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Mittwoch 03.10. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 04.10. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 10.10. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 11.10. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 17.10. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 18.10. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 24.10. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 25.10. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 31.10. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Mittwoch 07.11. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 08.11. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 14.11. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 15.11. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 21.11. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 22.11. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 28.11. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 29.11. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
UZA2 Hörsaal 8 (Raum 2Z206) 2.OG
Mittwoch 05.12. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 06.12. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 12.12. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 13.12. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 09.01. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 10.01. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 16.01. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 17.01. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 23.01. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 24.01. 11:30 - 13:00 Hörsaal III NIG Erdgeschoß
Mittwoch 30.01. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
Donnerstag 31.01. 11:30 - 13:00 Hörsaal 3, Währinger Straße 29 3.OG
Hörsaal III NIG Erdgeschoß
UZA2 Hörsaal 6 (Raum 2Z227) 2.OG

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 04.03.2019 18:07