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052300 VU Foundations of Data Analysis (2019S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 11.02.2019 09:00 to We 20.02.2019 23:59
- Deregistration possible until Su 17.03.2019 23:59
Details
max. 50 participants
Language: English
Lecturers
- Claudia Plant
- Sahar Behzadi Soheil
- Moritz Grosse-Wentrup
- Torsten Möller
- Benjamin Schelling
- Thomas Torsney-Weir
Classes (iCal) - next class is marked with N
- Wednesday 06.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Thursday 07.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Wednesday 13.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Thursday 14.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Wednesday 20.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Thursday 21.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Wednesday 27.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Thursday 28.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Wednesday 03.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Thursday 04.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Wednesday 10.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Thursday 11.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
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Thursday
02.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Wednesday
08.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Thursday
09.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß - Monday 13.05. 13:15 - 14:45 Seminarraum 4, Währinger Straße 29 1.UG
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Wednesday
15.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Thursday
16.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Wednesday
22.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Thursday
23.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Wednesday
29.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Wednesday
05.06.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Thursday
06.06.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Wednesday
12.06.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Thursday
13.06.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Wednesday
19.06.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Wednesday
26.06.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß -
Thursday
27.06.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal II NIG Erdgeschoß
Information
Aims, contents and method of the course
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.
Assessment and permitted materials
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.
Minimum requirements and assessment criteria
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 each of the 3 parts of the course.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 each of the 3 parts of the course.In order to successfully pass the course, regular attendance is strongly recommended, however not mandatory.
Examination topics
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
Reading list
> 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.
Association in the course directory
Module: FDA AKM SWI STW
Last modified: Mo 07.09.2020 15:30