052300 VU Foundations of Data Analysis (2018W)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Sa 08.09.2018 09:00 bis So 23.09.2018 23:59
- Abmeldung bis So 14.10.2018 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Mittwoch
03.10.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
04.10.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
10.10.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
11.10.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
17.10.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
18.10.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
24.10.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
25.10.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
31.10.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Mittwoch
07.11.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
08.11.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
14.11.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
15.11.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
21.11.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
22.11.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
28.11.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
29.11.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
UZA2 Hörsaal 8 (Raum 2Z206) 2.OG
UZA2 Hörsaal 8 (Raum 2Z206) 2.OG
Mittwoch
05.12.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
06.12.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
12.12.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
13.12.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
09.01.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
10.01.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
16.01.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
17.01.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
23.01.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
24.01.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Mittwoch
30.01.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Donnerstag
31.01.
11:30 - 13:00
Hörsaal 3, Währinger Straße 29 3.OG
Hörsaal III NIG Erdgeschoß
UZA2 Hörsaal 6 (Raum 2Z227) 2.OG
Hörsaal III NIG Erdgeschoß
UZA2 Hörsaal 6 (Raum 2Z227) 2.OG
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.
Art der Leistungskontrolle und erlaubte Hilfsmittel
3 labs (i.e. programming exercises including peer review), for each lab you will get a maximum of 12% of the required points.
- 2 pen-and-paper exercise sheets. They serve as a preparation for the tests. For each exercise sheet you will be able to get a maximum of 5% of the required points.
- 2 exams, one mid-term where you can obtain up to 20% of the total points and one final with questions on the entire course where you can obtain up to 30%.
Furthermore you can complete:
- 1 exercise sheet to assess your current mathematical (prerequisite) knowledge.
- 3 anonymized feedbacks (for a maximum of 3 feedbacks i.e. 1% for each feedback) These feedbacks can either be returned to the Tutor responsible for the lecture in an anonymized manner.
- 2 pen-and-paper exercise sheets. They serve as a preparation for the tests. For each exercise sheet you will be able to get a maximum of 5% of the required points.
- 2 exams, one mid-term where you can obtain up to 20% of the total points and one final with questions on the entire course where you can obtain up to 30%.
Furthermore you can complete:
- 1 exercise sheet to assess your current mathematical (prerequisite) knowledge.
- 3 anonymized feedbacks (for a maximum of 3 feedbacks i.e. 1% for each feedback) These feedbacks can either be returned to the Tutor responsible for the lecture in an anonymized manner.
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: Sa 02.04.2022 00:17