Universität Wien

052300 VU Foundations of Data Analysis (2024S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung
Moodle
Mi 05.06. 09:45-11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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

Art der Leistungskontrolle und erlaubte Hilfsmittel

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).

Mindestanforderungen und Beurteilungsmaßstab

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.

Prüfungsstoff

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

Literatur

> 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.

Zuordnung im Vorlesungsverzeichnis

Module: FDA AKM SWI STW

Letzte Änderung: Di 05.03.2024 17:05