052300 VU Foundations of Data Analysis (2020S)
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 10.02.2020 09:00 bis Do 20.02.2020 09:00
- Abmeldung bis Do 30.04.2020 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Mittwoch 04.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
- Donnerstag 05.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
- Mittwoch 11.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
- Mittwoch 18.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
- Donnerstag 19.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
- Mittwoch 25.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
- Donnerstag 26.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
- Mittwoch 01.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
- Donnerstag 02.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
- Mittwoch 22.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
- Donnerstag 23.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
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Mittwoch
29.04.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9 -
Donnerstag
30.04.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9 -
Mittwoch
06.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9 -
Donnerstag
07.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9 -
Mittwoch
13.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9 -
Donnerstag
14.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9 -
Mittwoch
20.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9 - Mittwoch 27.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
- Donnerstag 28.05. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
- Mittwoch 03.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
- Donnerstag 04.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
- Mittwoch 10.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
- Mittwoch 17.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
- Donnerstag 18.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
- Mittwoch 24.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
- Donnerstag 25.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 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%. UPDATE: The exams will be handled online via Moodle. For details please refer to the course page on Moodle. All exercises and exams are individual work.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 be returned to the Tutor responsible for the lecture in an anonymized manner
- in addition you can earn at most 10% of bonus points for completing voluntary exercises
- 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%. UPDATE: The exams will be handled online via Moodle. For details please refer to the course page on Moodle. All exercises and exams are individual work.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 be returned to the Tutor responsible for the lecture in an anonymized manner
- in addition you can earn at most 10% of bonus points for completing voluntary exercises
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: Mo 07.09.2020 15:20