052300 VU Foundations of Data Analysis (2017W)
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 09.09.2017 09:00 bis So 24.09.2017 23:59
- Abmeldung bis So 15.10.2017 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Mittwoch
04.10.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Freitag
06.10.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Mittwoch
11.10.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Freitag
13.10.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Mittwoch
18.10.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Freitag
20.10.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Mittwoch
25.10.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Freitag
27.10.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Freitag
03.11.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Mittwoch
08.11.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Freitag
10.11.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Mittwoch
15.11.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Freitag
17.11.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Mittwoch
22.11.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Donnerstag
23.11.
16:45 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Freitag
24.11.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Mittwoch
29.11.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Freitag
01.12.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Mittwoch
06.12.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Mittwoch
06.12.
18:30 - 20:00
Hörsaal 3, Währinger Straße 29 3.OG
Mittwoch
13.12.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Freitag
15.12.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Freitag
12.01.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Freitag
19.01.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Freitag
26.01.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
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.
- 3 pen and paper exercise sheets, one for each part of the VU. They serve as a preparation for the test. For each exercise sheet you will be able to get a maximum of 4% of the required points.
- 3 exams, 16% each, which amounts to 50% of the points in each part of the VU.
Furthermore you can complete:
- 1 exercise sheet to assess your current mathematical (prerequisite) knowledge.
- 3 anonymized feedbacks for each part of the VU. 3% (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, or to the Professors directly in an anonymized manner.
- 3 pen and paper exercise sheets, one for each part of the VU. They serve as a preparation for the test. For each exercise sheet you will be able to get a maximum of 4% of the required points.
- 3 exams, 16% each, which amounts to 50% of the points in each part of the VU.
Furthermore you can complete:
- 1 exercise sheet to assess your current mathematical (prerequisite) knowledge.
- 3 anonymized feedbacks for each part of the VU. 3% (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, or to the Professors directly 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. Specifically, you need to need to get 14.4% points (of the whole course) from Labs A0-A6, and 19.2% of the whole course from the 3 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. Specifically, you need to need to get 14.4% points (of the whole course) from Labs A0-A6, and 19.2% of the whole course from the 3 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: Mo 07.09.2020 15:30