052300 VU Foundations of Data Analysis (2025S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 10.02.2025 09:00 bis Fr 21.02.2025 09:00
- Abmeldung bis Fr 14.03.2025 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Mittwoch 05.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Donnerstag 06.03. 09:45 - 11:15 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
- Donnerstag 13.03. 09:45 - 11:15 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
- Mittwoch 19.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Donnerstag 20.03. 09:45 - 11:15 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
- Mittwoch 26.03. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Donnerstag 27.03. 09:45 - 11:15 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
- Mittwoch 02.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Donnerstag 03.04. 09:45 - 11:15 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
- Mittwoch 09.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Donnerstag 10.04. 09:45 - 11:15 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
- N Mittwoch 30.04. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Mittwoch 07.05. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
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Donnerstag
08.05.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8 - Mittwoch 14.05. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Donnerstag 15.05. 09:45 - 11:15 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
- Mittwoch 21.05. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Donnerstag 22.05. 09:45 - 11:15 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
- Mittwoch 28.05. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Mittwoch 04.06. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Donnerstag 05.06. 09:45 - 11:15 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
- Mittwoch 11.06. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Donnerstag 12.06. 09:45 - 11:15 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
- Mittwoch 18.06. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
- Mittwoch 25.06. 09:45 - 11:15 Hörsaal II NIG Erdgeschoß
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Donnerstag
26.06.
09:45 - 11:15
Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
Seminarraum 2, Währinger Straße 29 1.UG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
PLEASE NOTE THAT ATTENDANCE IS MANDATORY IN THE FIRST SESSION.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.The lecture will be onsite. Parts of the lecture will be recorded and/or streamed. Details will be announced on Moodle.
Art der Leistungskontrolle und erlaubte Hilfsmittel
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.
- 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 27.5% of the total points. These exams are planned for May, 8 and June, 26.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)
- for the preparation of the exams, voluntary exercises will be providedIf not differently specified, the assignments need to be solved individually, sharing your solutions and copying those of other students is forbidden. Also note that if not differently specified, the usage of generative AI and other AI-assisted technologies are not allowed. 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).For each of the assignments, the authorized aids will be communicated in the description of the corresponding assignment.
- 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.
- 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 27.5% of the total points. These exams are planned for May, 8 and June, 26.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)
- for the preparation of the exams, voluntary exercises will be providedIf not differently specified, the assignments need to be solved individually, sharing your solutions and copying those of other students is forbidden. Also note that if not differently specified, the usage of generative AI and other AI-assisted technologies are not allowed. 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).For each of the assignments, the authorized aids will be communicated in the description of the corresponding assignment.
Mindestanforderungen und Beurteilungsmaßstab
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.
Prüfungsstoff
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
Literatur
> 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.
Zuordnung im Vorlesungsverzeichnis
Module: FDA AKM SWI STW
Letzte Änderung: Fr 28.02.2025 15:45