Universität Wien FIND
Please note: From 11 March 2020 until 4 April 2020, no courses/exams with student attendance will take place at the University of Vienna. This applies to courses and exams in all degree programmes. Teaching will instead take place in the form of learning at home and e-learning. For further information, please visit our website at https://www.univie.ac.at/en/about-us/further-information/coronavirus/.

052300 VU Foundations of Data Analysis (2020S)

Continuous assessment of course work

Registration/Deregistration

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Wednesday 04.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 05.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 11.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 18.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 19.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 25.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 26.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 02.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 22.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 23.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 29.04. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 30.04. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 06.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 07.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 13.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 14.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 20.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 27.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 28.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 03.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 04.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 10.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 17.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 18.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday 24.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday 25.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9

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%. For the exams no auxiliary materials are allowed.
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 be returned to the Tutor responsible for the lecture in an anonymized manner
- in addition you can earn at most 10% of bonus points for completing voluntary exercises

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: Th 26.03.2020 18:47