052300 VU Foundations of Data Analysis (2021S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 15.02.2021 09:00 bis Mo 22.02.2021 09:00
- Abmeldung bis So 14.03.2021 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
- Claudia Plant
- Moritz Grosse-Wentrup
- Akshey Kumar
- Alex Markham
- Lukas Miklautz
- Ylli Sadikaj
- Erion Çano
Termine (iCal) - nächster Termin ist mit N markiert
Please use the following link to the first virtual meeting on March, 3 if you have no Moodle access.
https://univienna.zoom.us/j/93101135920?pwd=ZnRBMG9IQ28rdVdCU0s0YTB3V2Y2Zz09Meeting-ID: 931 0113 5920access code: 752028
Mittwoch
03.03.
09:45 - 11:15
Digital
Donnerstag
04.03.
09:45 - 11:15
Digital
Mittwoch
10.03.
09:45 - 11:15
Digital
Donnerstag
11.03.
09:45 - 11:15
Digital
Mittwoch
17.03.
09:45 - 11:15
Digital
Donnerstag
18.03.
09:45 - 11:15
Digital
Mittwoch
24.03.
09:45 - 11:15
Digital
Donnerstag
25.03.
09:45 - 11:15
Digital
Mittwoch
14.04.
09:45 - 11:15
Digital
Donnerstag
15.04.
09:45 - 11:15
Digital
Mittwoch
21.04.
09:45 - 11:15
Digital
Donnerstag
22.04.
09:45 - 11:15
Digital
Mittwoch
28.04.
09:45 - 11:15
Digital
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Donnerstag
29.04.
09:45 - 11:15
Digital
Mittwoch
05.05.
09:45 - 11:15
Digital
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Donnerstag
06.05.
09:45 - 11:15
Digital
Mittwoch
12.05.
09:45 - 11:15
Digital
Mittwoch
19.05.
09:45 - 11:15
Digital
Donnerstag
20.05.
09:45 - 11:15
Digital
Mittwoch
26.05.
09:45 - 11:15
Digital
Donnerstag
27.05.
09:45 - 11:15
Digital
Mittwoch
02.06.
09:45 - 11:15
Digital
Mittwoch
09.06.
09:45 - 11:15
Digital
Donnerstag
10.06.
09:45 - 11:15
Digital
Mittwoch
16.06.
09:45 - 11:15
Digital
Donnerstag
17.06.
09:45 - 11:15
Digital
Mittwoch
23.06.
09:45 - 11:15
Digital
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Donnerstag
24.06.
09:45 - 11:15
Digital
Mittwoch
30.06.
09:45 - 11:15
Digital
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
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.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. Details will be announced on Moodle.
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: Fr 12.05.2023 00:13