052300 VU Foundations of Data Analysis (2022S)
Prüfungsimmanente Lehrveranstaltung
Labels
GEMISCHT
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 14.02.2022 09:00 bis Do 24.02.2022 10:00
- Abmeldung bis Mo 14.03.2022 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
The lectures of 02.03. and 03.03. will be digital.
Please find here the Zoom-link for the first lecture on March, 2:
https://univienna.zoom.us/j/63151141628?pwd=bmNTb3BFcnlUZ056YnVqMnVCUGdhQT09
Meeting-ID: 631 5114 1628
access code: 165159
https://univienna.zoom.us/j/65992634287?pwd=cTZlakEzK05LZ2J3cVc2alpQN2JEQT09
Meeting-ID: 659 9263 4287
access code: 163392
Mittwoch
02.03.
09:45 - 11:15
Digital
Hörsaal II NIG Erdgeschoß
Hörsaal II NIG Erdgeschoß
Donnerstag
03.03.
09:45 - 11:15
BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
Mittwoch
09.03.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
10.03.
09:45 - 11:15
BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
Mittwoch
16.03.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
17.03.
09:45 - 11:15
BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
Mittwoch
23.03.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
24.03.
09:45 - 11:15
BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
Mittwoch
30.03.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
31.03.
09:45 - 11:15
BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
Mittwoch
06.04.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
07.04.
09:45 - 11:15
BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
Mittwoch
27.04.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
28.04.
09:45 - 11:15
BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
Mittwoch
04.05.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
05.05.
09:45 - 11:15
Digital
Mittwoch
11.05.
09:45 - 11:15
Digital
Donnerstag
12.05.
09:45 - 11:15
Digital
Mittwoch
18.05.
09:45 - 11:15
Digital
Donnerstag
19.05.
09:45 - 11:15
BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
Mittwoch
25.05.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Mittwoch
01.06.
09:45 - 11:15
Digital
Donnerstag
02.06.
09:45 - 11:15
Digital
Mittwoch
08.06.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
09.06.
09:45 - 11:15
BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
Mittwoch
15.06.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Mittwoch
22.06.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
23.06.
09:45 - 11:15
BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
Mittwoch
29.06.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
30.06.
16:30 - 18:00
Digital
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
- 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
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%Please keep in mind that in order to pass the course, you will need at least 30% of the total score in all labs and homeworks combined with 40% of the total score of the tests.In order to successfully pass the course, regular attendance is strongly recommended, however not mandatory.
- 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%Please keep in mind that in order to pass the course, you will need at least 30% of the total score in all labs and homeworks combined with 40% of the total score of the tests.In order 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
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
> 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.
> 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.
Zuordnung im Vorlesungsverzeichnis
Module: FDA AKM SWI STW
Letzte Änderung: Do 11.05.2023 11:27
https://univienna.zoom.us/j/63151141628?pwd=bmNTb3BFcnlUZ056YnVqMnVCUGdhQT09
Meeting-ID: 631 5114 1628
access code: 165159Zoom-link to the second lecture on March, 3:
https://univienna.zoom.us/j/65992634287?pwd=cTZlakEzK05LZ2J3cVc2alpQN2JEQT09
Meeting-ID: 659 9263 4287
access code: 163392