Universität Wien FIND

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052300 VU Foundations of Data Analysis (2021W)

Continuous assessment of course work
MIXED

Registration/Deregistration

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

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Thursday 07.10. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 13.10. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 14.10. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 20.10. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 21.10. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 27.10. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 28.10. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 03.11. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 04.11. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 10.11. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 11.11. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 17.11. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 18.11. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 24.11. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 25.11. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 01.12. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 02.12. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Thursday 09.12. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 15.12. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 16.12. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 12.01. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 13.01. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 19.01. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 20.01. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Wednesday 26.01. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
Thursday 27.01. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4

Information

Aims, contents and method of the course

lecture format: hybrid

The first session will be digital. The zoom link will be posted in Moodle and here. We will inform you about the format over Moodle and here.

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.

Due to the ongoing pandemic, we will adopt a mixed lecture format that consists of pre-recorded video lectures with live (offline or online) lectures and review sessions. New video lectures, tutorials and other learning materials will be made available on Moodle on an ongoing basis. These materials form the basis for the review sessions and lectures, which will either be held in-person in the lecture hall or online during the official lecture times. In the review sessions, we will review the most important concepts introduced in the videos and answer any questions you may have.

Assessment and permitted materials

- 2 labs (i.e. programming exercises including peer review), for each lab you will get a maximum of 18% of the required points.

- 2 pen-and-paper exercise sheets. They serve as a preparation for the exams. For each exercise sheet you will be able to get a maximum of 5% of the required points.

- 2 exams, one mid-term and one final, each 25% of the total points.

Furthermore you can complete:

- 1 exercise sheet to assess your current mathematical (prerequisite) knowledge, 1% of the total points.

- 3 anonymized feedbacks, each 1% of the total points.

- in addition you can earn at most 10% of bonus points for completing voluntary quizzes

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%

To pass the course, you need at least 30% of the total score in all assignments combined with 40% of the total score of the exams.

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. 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. Partitioning Clustering
6.2. Hierarchical Clustering
6.3. Density-based Clustering
6.4. Evaluation and Validation of Clustering Results

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.

Shai Shalev-Shwartz, Shai Ben-David: Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014.

Association in the course directory

Module: FDA AKM SWI STW

Last modified: Tu 21.09.2021 12:48