052300 VU Foundations of Data Analysis (2021W)
Prüfungsimmanente Lehrveranstaltung
Labels
GEMISCHT
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 13.09.2021 09:00 bis Mo 20.09.2021 09:00
- Abmeldung bis Do 14.10.2021 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Mittwoch
06.10.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Donnerstag
07.10.
11:30 - 13:00
Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Mittwoch
13.10.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Donnerstag
14.10.
11:30 - 13:00
Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Mittwoch
20.10.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Donnerstag
21.10.
11:30 - 13:00
Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Mittwoch
27.10.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Donnerstag
28.10.
11:30 - 13:00
Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Mittwoch
03.11.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Donnerstag
04.11.
11:30 - 13:00
Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Mittwoch
10.11.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Donnerstag
11.11.
11:30 - 13:00
Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Mittwoch
17.11.
09:45 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Donnerstag
18.11.
11:30 - 13:00
Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Mittwoch
24.11.
09:45 - 11:15
Digital
Donnerstag
25.11.
11:30 - 13:00
Digital
Mittwoch
01.12.
09:45 - 11:15
Digital
Donnerstag
02.12.
11:30 - 13:00
Digital
Donnerstag
09.12.
11:30 - 13:00
Digital
Mittwoch
15.12.
09:45 - 11:15
Digital
Donnerstag
16.12.
11:30 - 13:00
Digital
Mittwoch
12.01.
09:45 - 11:15
Digital
Donnerstag
13.01.
11:30 - 13:00
Digital
Mittwoch
19.01.
09:45 - 11:15
Digital
Donnerstag
20.01.
11:30 - 13:00
Digital
Mittwoch
26.01.
09:45 - 11:15
Digital
Donnerstag
27.01.
11:30 - 13:00
Digital
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
- 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.
Exams:
- Midterm 18.11.2021
- Final: 27.01.2022
Both exams will be digital.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
Exams:
- Midterm 18.11.2021
- Final: 27.01.2022
Both exams will be digital.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
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%To pass the course, you need at least 30% of the total score in all assignments combined with 30% of the total score of the exams.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%To pass the course, you need at least 30% of the total score in all assignments combined with 30% of the total score of the exams.In order 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. 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. Partitioning Clustering
6.2. Hierarchical Clustering
6.3. Density-based Clustering
6.4. Evaluation and Validation of Clustering Results
1.1. Fundamental Concepts in Inference
1.2. Parametric Inference
1.3. 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. Partitioning Clustering
6.2. Hierarchical Clustering
6.3. Density-based Clustering
6.4. Evaluation and Validation of Clustering Results
Literatur
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.Shai Shalev-Shwartz, Shai Ben-David: Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014.
Zuordnung im Vorlesungsverzeichnis
Module: FDA AKM SWI STW
Letzte Änderung: Fr 12.05.2023 00:13
Wed 13 Oct: https://eu.bbcollab.com/guest/1bb7b67ccec24e1683e813de9d6d5ca9
Thu 14 Oct:
https://eu.bbcollab.com/guest/6e1466238330468ebd5e9ca096acce14We will inform you about the format of all future sessions here and in Moodle.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.Due to the ongoing pandemic, we will adopt a mixed lecture format that consists of pre-recorded video lectures with live (offline or online) lectures and review sessions. New video lectures, tutorials and other learning materials will be made available on Moodle on an ongoing basis. These materials form the basis for the review sessions and lectures, which will either be held in-person in the lecture hall or online during the official lecture times. In the review sessions, we will review the most important concepts introduced in the videos and answer any questions you may have.