052300 VU Foundations of Data Analysis (2018W)
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 08.09.2018 09:00 to Su 23.09.2018 23:59
- Deregistration possible until Su 14.10.2018 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Wednesday
03.10.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
04.10.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
10.10.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
11.10.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
17.10.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
18.10.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
24.10.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
25.10.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
31.10.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Wednesday
07.11.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
08.11.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
14.11.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
15.11.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
21.11.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
22.11.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
28.11.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
29.11.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
UZA2 Hörsaal 8 (Raum 2Z206) 2.OG
UZA2 Hörsaal 8 (Raum 2Z206) 2.OG
Wednesday
05.12.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
06.12.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
12.12.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
13.12.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
09.01.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
10.01.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
16.01.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
17.01.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
23.01.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
24.01.
11:30 - 13:00
Hörsaal III NIG Erdgeschoß
Wednesday
30.01.
09:45 - 11:15
Hörsaal II NIG Erdgeschoß
Thursday
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
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 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.
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: Sa 02.04.2022 00:17