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052300 VU Foundations of Data Analysis (2020W)
Continuous assessment of course work
Labels
Registration/Deregistration
- Registration is open from Mo 14.09.2020 09:00 to Mo 21.09.2020 09:00
- Deregistration possible until We 14.10.2020 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Thursday
01.10.
11:30 - 13:00
Digital
Wednesday
07.10.
09:45 - 11:15
Digital
Thursday
08.10.
11:30 - 13:00
Digital
Wednesday
14.10.
09:45 - 11:15
Digital
Thursday
15.10.
11:30 - 13:00
Digital
Wednesday
21.10.
09:45 - 11:15
Digital
Thursday
22.10.
11:30 - 13:00
Digital
Wednesday
28.10.
09:45 - 11:15
Digital
Thursday
29.10.
11:30 - 13:00
Digital
Wednesday
04.11.
09:45 - 11:15
Digital
Thursday
05.11.
11:30 - 13:00
Digital
Wednesday
11.11.
09:45 - 11:15
Digital
Thursday
12.11.
11:30 - 13:00
Digital
Wednesday
18.11.
09:45 - 11:15
Digital
Thursday
19.11.
11:30 - 13:00
Digital
Wednesday
25.11.
09:45 - 11:15
Digital
Thursday
26.11.
11:30 - 13:00
Digital
Wednesday
02.12.
09:45 - 11:15
Digital
Thursday
03.12.
11:30 - 13:00
Digital
Wednesday
09.12.
09:45 - 11:15
Digital
Thursday
10.12.
11:30 - 13:00
Digital
Wednesday
16.12.
09:45 - 11:15
Digital
Thursday
17.12.
11:30 - 13:00
Digital
Thursday
07.01.
11:30 - 13:00
Digital
Wednesday
13.01.
09:45 - 11:15
Digital
Thursday
14.01.
11:30 - 13:00
Digital
N
Wednesday
20.01.
09:45 - 11:15
Digital
Thursday
21.01.
11:30 - 13:00
Digital
Wednesday
27.01.
09:45 - 11:15
Digital
Thursday
28.01.
11:30 - 13:00
Digital
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%To pass the course, you need at least 30% of the total score in all assignments combined with 40% 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 40% of the total score of the exams.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. 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
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.Shai Shalev-Shwartz, Shai Ben-David: Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014.
Association in the course directory
Module: FDA AKM SWI STW
Last modified: Tu 12.01.2021 09:07
Concepts as well as techniques are introduced and practiced.Due to the ongoing pandemic, we will adopt a mixed lecture format that complements pre-recorded video lectures with live (offline or online) review sessions. New video lectures and tutorials will be made available on Moodle on an ongoing basis. These videos form the basis for the review sessions, which will either be held in-person in the lecture hall or online via Big Blue Button sessions 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.