Universität Wien

052300 VU Foundations of Data Analysis (2024S)

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 06.03. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 07.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 13.03. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 14.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 20.03. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 21.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 10.04. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 11.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 17.04. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 18.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 24.04. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 25.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 02.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 08.05. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
    Hörsaal II NIG Erdgeschoß
  • Wednesday 15.05. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 16.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 22.05. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 23.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 29.05. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Wednesday 05.06. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 06.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 12.06. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 13.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 19.06. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 20.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 26.06. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 27.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
    Hörsaal II NIG Erdgeschoß

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.

The lecture will be onsite. Parts of the lecture will be recorded and/or streamed. Details will be announced on Moodle.

You can attend the introductory lecture on March, 6 over Zoom:
https://univienna.zoom.us/j/68717730293?pwd=KzZzbU9DRkFaRHdZYUlmajJxdjdXdz09
Meeting-ID: 687 1773 0293
Access Code: 995120

Assessment and permitted materials

Assignments:
- 2 labs: i.e. programming exercises including peer review. For each lab you will be able to get a maximum of 18% of the total points.
- 2 pen-and-paper assignments: they serve as a preparation for the exams. For each exercise sheet you will be able to get a maximum of 5% of the total points.
- 1 mathematical prerequisites test: exercise sheet to assess your current mathematical knowledge (prerequisite), 1% of the total points.
- 3 anonymized feedback forms, each 1% of the total points.

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

Bonus exercises:
- in addition you can earn at most 10% of bonus points for completing voluntary quizzes (maximum 5% for each part of the lecture)

The assignments need to be solved individually, sharing your solutions and copying those of other students is forbidden.
For each of these assignments, the authorized aids will be communicated in the description of the corresponding assignment.
Proper marking and citing of all external materials you used is mandatory. We will make use of plagiarism and code checking tools (e.g. Turnitin).

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%

+ In order to pass the course, you will need at least 30% of the total points of the assignments AND 30% of the total points of the exams.

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

> Shalev-Shwartz, Shai, and Shai Ben-David. Understanding machine learning: From theory to algorithms. Cambridge University Press, 2014.
> Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006.
> Hastie-Tibshirani-Friedman: The Elements of Statistical Learning, Springer 2009.
> Han, Kamber, Pei: Data Mining Concepts and Techniques Third Edition.
> Witten, Frank, Hall, Pai: Data Mining Practical Machine Learning Tools and Techniques Fourth Edition.

Association in the course directory

Module: FDA AKM SWI STW

Last modified: Tu 05.03.2024 17:05