Universität Wien

052300 VU Foundations of Data Analysis (2024W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

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

  • Wednesday 02.10. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 03.10. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 09.10. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 10.10. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 16.10. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 17.10. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 23.10. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 24.10. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 30.10. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 31.10. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 06.11. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 07.11. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 13.11. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 14.11. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 20.11. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 21.11. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
    Seminarraum 7, Währinger Straße 29 1.OG
  • Wednesday 27.11. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 28.11. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 04.12. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 05.12. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 11.12. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 12.12. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 08.01. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 09.01. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 15.01. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 16.01. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 22.01. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 23.01. 11:30 - 13:00 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
  • Wednesday 29.01. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 30.01. 11:30 - 13:00 Hörsaal 1 Hauptgebäude Tiefparterre Stiege 1 Hof 1
    Hörsaal 21 Hauptgebäude, Hochparterre, 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.

Methods: lectures, of pre-recorded video lectures, live lectures and review sessions. New video lectures, tutorials and other learning materials will be made available on Moodle on an ongoing basis. In the review sessions, we will review the most important concepts introduced in the videos and answer any questions you may have.

You can attend the introductory lecture on October, 2 over Zoom:
https://univienna.zoom.us/j/63287522507?pwd=kPh4TwTPZZgDG8Q2xJt8voQ4YUZDjl.1
Meeting-ID: 632 8752 2507
Access Code: 995120

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 30% of the total score of the exams.

In order to successfully pass the course, regular attendance is strongly recommended, however not mandatory.

For the assignments no aids are allowed.

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 12.11.2024 09:05