Universität Wien FIND

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052300 VU Foundations of Data Analysis (2017W)

Continuous assessment of course work

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first serve).

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Wednesday 04.10. 15:00 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Friday 06.10. 13:15 - 16:30 Hörsaal 1, Währinger Straße 29 1.UG
Wednesday 11.10. 15:00 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Friday 13.10. 13:15 - 16:30 Hörsaal 1, Währinger Straße 29 1.UG
Wednesday 18.10. 15:00 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Friday 20.10. 13:15 - 16:30 Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Wednesday 25.10. 15:00 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Friday 27.10. 13:15 - 16:30 Hörsaal 1, Währinger Straße 29 1.UG
Friday 03.11. 13:15 - 16:30 Hörsaal 1, Währinger Straße 29 1.UG
Wednesday 08.11. 15:00 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Friday 10.11. 13:15 - 16:30 Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Wednesday 15.11. 15:00 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Friday 17.11. 13:15 - 16:30 Hörsaal 1, Währinger Straße 29 1.UG
Wednesday 22.11. 15:00 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Thursday 23.11. 16:45 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Friday 24.11. 13:15 - 16:30 Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Wednesday 29.11. 15:00 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Friday 01.12. 13:15 - 16:30 Hörsaal 1, Währinger Straße 29 1.UG
Wednesday 06.12. 15:00 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 06.12. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 13.12. 15:00 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG
Friday 15.12. 13:15 - 16:30 Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Friday 12.01. 13:15 - 16:30 Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Friday 19.01. 13:15 - 16:30 Hörsaal 1, Währinger Straße 29 1.UG
Friday 26.01. 13:15 - 16:30 Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8

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

3 labs (i.e. programming exercises including peer review), for each lab you will get a maximum of 12% of the required points.
- 3 pen and paper exercise sheets, one for each part of the VU. They serve as a preparation for the test. For each exercise sheet you will be able to get a maximum of 4% of the required points.
- 3 exams, 16% each, which amounts to 50% of the points in each part of the VU.
Furthermore you can complete:
- 1 exercise sheet to assess your current mathematical (prerequisite) knowledge.
- 3 anonymized feedbacks for each part of the VU. 3% (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, or to the Professors directly in an anonymized manner.

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

Module: FDA AKM SWI STW

Last modified: Mo 07.09.2020 15:30