Universität Wien FIND
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052300 VU Foundations of Data Analysis (2018W)

Continuous assessment of course work

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

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

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.
- 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.

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)

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.

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 04.03.2019 18:07