052300 VU Foundations of Data Analysis (2024S)
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 12.02.2024 09:00 to Th 22.02.2024 09:00
- Deregistration possible until Th 14.03.2024 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Wednesday
06.03.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
07.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
13.03.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
14.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
20.03.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
21.03.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
10.04.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
11.04.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
17.04.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
18.04.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
24.04.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
25.04.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
N
Thursday
02.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
08.05.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Hörsaal II NIG Erdgeschoß
Hörsaal II NIG Erdgeschoß
Wednesday
15.05.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
16.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
22.05.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
23.05.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
29.05.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Wednesday
05.06.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
06.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
12.06.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
13.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
19.06.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
20.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Wednesday
26.06.
09:45 - 11:15
Hörsaal C2 UniCampus Hof 2 2G-K1-03
Thursday
27.06.
09:45 - 11:15
Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
Hörsaal II NIG Erdgeschoß
Hörsaal II NIG Erdgeschoß
Information
Aims, contents and method of the course
Assessment and permitted materials
Assignments:
- 2 labs: i.e. programming exercises including peer review. For each lab you will be able to get a maximum of 18% of the total points.
- 2 pen-and-paper assignments: they serve as a preparation for the exams. For each exercise sheet you will be able to get a maximum of 5% of the total points.
- 1 mathematical prerequisites test: exercise sheet to assess your current mathematical knowledge (prerequisite), 1% of the total points.
- 3 anonymized feedback forms, each 1% of the total points.Exams:
- 2 exams: one mid-term and one final exam, each 25% of the total points.Bonus exercises:
- in addition you can earn at most 10% of bonus points for completing voluntary quizzes (maximum 5% for each part of the lecture)The assignments need to be solved individually, sharing your solutions and copying those of other students is forbidden.
For each of these assignments, the authorized aids will be communicated in the description of the corresponding assignment.
Proper marking and citing of all external materials you used is mandatory. We will make use of plagiarism and code checking tools (e.g. Turnitin).
- 2 labs: i.e. programming exercises including peer review. For each lab you will be able to get a maximum of 18% of the total points.
- 2 pen-and-paper assignments: they serve as a preparation for the exams. For each exercise sheet you will be able to get a maximum of 5% of the total points.
- 1 mathematical prerequisites test: exercise sheet to assess your current mathematical knowledge (prerequisite), 1% of the total points.
- 3 anonymized feedback forms, each 1% of the total points.Exams:
- 2 exams: one mid-term and one final exam, each 25% of the total points.Bonus exercises:
- in addition you can earn at most 10% of bonus points for completing voluntary quizzes (maximum 5% for each part of the lecture)The assignments need to be solved individually, sharing your solutions and copying those of other students is forbidden.
For each of these assignments, the authorized aids will be communicated in the description of the corresponding assignment.
Proper marking and citing of all external materials you used is mandatory. We will make use of plagiarism and code checking tools (e.g. Turnitin).
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%+ In order to pass the course, you will need at least 30% of the total points of the assignments AND 30% of the total points of the exams.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%+ In order to pass the course, you will need at least 30% of the total points of the assignments AND 30% of the total points of the exams.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-Validation2. Regression Modelling
2.1. Simple Linear Regression
2.2. Multiple Regression
2.3. Further Regression Methods
2.4. Generalized Linear Models
2.5. Regression Trees3. 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 Methods4. Neural Networks5. Basic Techniques of Unsupervised Learning
5.1. Dimension Reduction (Matrix Factorization)
5.2. Association Rules6. 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-Validation2. Regression Modelling
2.1. Simple Linear Regression
2.2. Multiple Regression
2.3. Further Regression Methods
2.4. Generalized Linear Models
2.5. Regression Trees3. 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 Methods4. Neural Networks5. Basic Techniques of Unsupervised Learning
5.1. Dimension Reduction (Matrix Factorization)
5.2. Association Rules6. 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
> Shalev-Shwartz, Shai, and Shai Ben-David. Understanding machine learning: From theory to algorithms. Cambridge University Press, 2014.
> Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006.
> Hastie-Tibshirani-Friedman: The Elements of Statistical Learning, Springer 2009.
> Han, Kamber, Pei: Data Mining Concepts and Techniques Third Edition.
> Witten, Frank, Hall, Pai: Data Mining Practical Machine Learning Tools and Techniques Fourth Edition.
> Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006.
> Hastie-Tibshirani-Friedman: The Elements of Statistical Learning, Springer 2009.
> Han, Kamber, Pei: Data Mining Concepts and Techniques Third Edition.
> Witten, Frank, Hall, Pai: Data Mining Practical Machine Learning Tools and Techniques Fourth Edition.
Association in the course directory
Module: FDA AKM SWI STW
Last modified: Tu 05.03.2024 17:05
https://univienna.zoom.us/j/68717730293?pwd=KzZzbU9DRkFaRHdZYUlmajJxdjdXdz09
Meeting-ID: 687 1773 0293
Access Code: 995120