Universität Wien FIND

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Further information about on-site teaching can be found at https://studieren.univie.ac.at/en/info.

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052300 VU Foundations of Data Analysis (2020W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle
We 20.01. 09:45-11:15 Digital

Registration/Deregistration

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Thursday 01.10. 11:30 - 13:00 Digital
Wednesday 07.10. 09:45 - 11:15 Digital
Thursday 08.10. 11:30 - 13:00 Digital
Wednesday 14.10. 09:45 - 11:15 Digital
Thursday 15.10. 11:30 - 13:00 Digital
Wednesday 21.10. 09:45 - 11:15 Digital
Thursday 22.10. 11:30 - 13:00 Digital
Wednesday 28.10. 09:45 - 11:15 Digital
Thursday 29.10. 11:30 - 13:00 Digital
Wednesday 04.11. 09:45 - 11:15 Digital
Thursday 05.11. 11:30 - 13:00 Digital
Wednesday 11.11. 09:45 - 11:15 Digital
Thursday 12.11. 11:30 - 13:00 Digital
Wednesday 18.11. 09:45 - 11:15 Digital
Thursday 19.11. 11:30 - 13:00 Digital
Wednesday 25.11. 09:45 - 11:15 Digital
Thursday 26.11. 11:30 - 13:00 Digital
Wednesday 02.12. 09:45 - 11:15 Digital
Thursday 03.12. 11:30 - 13:00 Digital
Wednesday 09.12. 09:45 - 11:15 Digital
Thursday 10.12. 11:30 - 13:00 Digital
Wednesday 16.12. 09:45 - 11:15 Digital
Thursday 17.12. 11:30 - 13:00 Digital
Thursday 07.01. 11:30 - 13:00 Digital
Wednesday 13.01. 09:45 - 11:15 Digital
Thursday 14.01. 11:30 - 13:00 Digital
Thursday 21.01. 11:30 - 13:00 Digital
Wednesday 27.01. 09:45 - 11:15 Digital
Thursday 28.01. 11:30 - 13:00 Digital

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.

Due to the ongoing pandemic, we will adopt a mixed lecture format that complements pre-recorded video lectures with live (offline or online) review sessions. New video lectures and tutorials will be made available on Moodle on an ongoing basis. These videos form the basis for the review sessions, which will either be held in-person in the lecture hall or online via Big Blue Button sessions 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 12.01.2021 09:07