Universität Wien FIND
Warning! The directory is not yet complete and will be amended until the beginning of the term.

052300 VU Foundations of Data Analysis (2016W)

Continuous assessment of course work

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Monday 03.10. 13:15 - 14:45 Hörsaal 1, Währinger Straße 29 1.UG (Kickoff Class)
Wednesday 05.10. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Monday 10.10. 13:15 - 14:45 (ehem. Hörsaal 23 Hauptgebäude, 1.Stock, Stiege 5)
Wednesday 12.10. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Monday 17.10. 13:15 - 14:45 (ehem. Hörsaal 23 Hauptgebäude, 1.Stock, Stiege 5)
Wednesday 19.10. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Monday 24.10. 13:15 - 14:45 (ehem. Hörsaal 23 Hauptgebäude, 1.Stock, Stiege 5)
Monday 31.10. 13:15 - 14:45 (ehem. Hörsaal 23 Hauptgebäude, 1.Stock, Stiege 5)
Monday 07.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 09.11. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Monday 14.11. 13:15 - 14:45 (ehem. Hörsaal 23 Hauptgebäude, 1.Stock, Stiege 5)
Wednesday 16.11. 08:00 - 09:30 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 16.11. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Monday 21.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 23.11. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Monday 28.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 30.11. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Monday 05.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 07.12. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Friday 09.12. 15:00 - 16:30 Seminarraum 8, Währinger Straße 29 1.OG
Monday 12.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 14.12. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 11.01. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 18.01. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 25.01. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Monday 30.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG

Information

Aims, contents and method of the course

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.

Assessment and permitted materials

Minimum requirements and assessment criteria

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)

Examination topics

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

Reading list

> 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.


Association in the course directory

Last modified: Fr 31.08.2018 08:48