052300 VU Foundations of Data Analysis (2023S)
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 13.02.2023 09:00 to Th 23.02.2023 09:00
- Deregistration possible until Tu 14.03.2023 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Wednesday
01.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
02.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
08.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
09.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
15.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
16.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
22.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
23.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
29.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
30.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
19.04.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
20.04.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
26.04.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
27.04.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
03.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
UZA2 Hörsaal 4 (Raum 2Z221) 2.OG
UZA2 Hörsaal 4 (Raum 2Z221) 2.OG
Thursday
04.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
10.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
11.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
17.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
24.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
25.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
31.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
01.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
07.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
14.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
15.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
21.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
22.06.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
Wednesday
28.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Thursday
29.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Information
Aims, contents and method of the course
Assessment and permitted materials
- 2 labs (i.e. programming exercises including peer review), for each lab you will get a maximum of 18% of the required points.- 2 pen-and-paper exercise sheets. They serve as a preparation for the exams. For each exercise sheet you will be able to get a maximum of 5% of the required points.- 2 exams, one mid-term and one final, each 25% of the total points.Furthermore you can complete:- 1 exercise sheet to assess your current mathematical (prerequisite) knowledge, 1% of the total points.- 3 anonymized feedbacks, each 1% of the total points.- in addition you can earn at most 10% of bonus points for completing voluntary quizzes
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: Tu 13.06.2023 10:27
https://univienna.zoom.us/j/65441749610?pwd=NStES0FSTXQzWUx6cVJzd1d5Qmo5UT09
Meeting-ID: 634 5494 8472
Code: 884794