052300 VU Foundations of Data Analysis (2017W)
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 Sa 09.09.2017 09:00 to Su 24.09.2017 23:59
- Deregistration possible until Su 15.10.2017 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Wednesday
04.10.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Friday
06.10.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Wednesday
11.10.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Friday
13.10.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Wednesday
18.10.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Friday
20.10.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Wednesday
25.10.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Friday
27.10.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Friday
03.11.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Wednesday
08.11.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Friday
10.11.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Wednesday
15.11.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Friday
17.11.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Wednesday
22.11.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Thursday
23.11.
16:45 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Friday
24.11.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Wednesday
29.11.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Friday
01.12.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Wednesday
06.12.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Wednesday
06.12.
18:30 - 20:00
Hörsaal 3, Währinger Straße 29 3.OG
Wednesday
13.12.
15:00 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
Friday
15.12.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Friday
12.01.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
Friday
19.01.
13:15 - 16:30
Hörsaal 1, Währinger Straße 29 1.UG
Friday
26.01.
13:15 - 16:30
Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
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.
- 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.
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)
- StEOP
- Programmierung 2 (PR2)
- Mathematische Grundlagen der Informatik 2 (MG2)
- Theoretische Informatik (THI)
- Modellierung (MOD)
- Algorithmen und Datenstrukturen (ADS)
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