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Data Analytics

Today{\uc1\u8217*}s business world is characterized by an unprecedented growth of data, by 2020 we will experience a 300-fold increase from 2005. This data comes in a broad variety of forms: 420 million wearable health monitors are currently in use, more than 4 billion hours of video are watched on YouTube each month and 30 billion pieces of content are shared on Facebook every month. A lot of the data is analyzed in real time: modern cars have about 100 sensors and the NYSE captures 1 TB of trade information during each trading session. However, 1 in 3 business leaders don{\uc1\u8217*}t trust the information they use to make decisions and about 27% of respondents in one survey were unsure of how much of their data was inaccurate. 4.4 million IT jobs have been created globally to support big data. AlphaGo has recently beaten the reigning (human) Go champion.
Kurs ID
CORE801
Art des Kurses
Promotion LV
Wochenstunden
2,0
Semester
FS 2023
Vortragssprache
Englisch
Vortragende/r
Prof. Dr. Stefan Spinler
Bitte beachten Sie, dass AustauschstudentInnen im BSc-Programm der WHU eine höhere Anzahl an Credits erwerben als hier aufgeführt. Für weitere Informationen wenden Sie sich bitte direkt an das [International Relations Office].
Part 01: Supervised learning

The following methods will be introduced and implemented in R with applications to real data:

  • Linear regression
  • Penalized regression
  • Logistic regression
  • CART
  • Random forests
  • Boosting
  • Support vector machines

Part 02: Unsupervised learning

The following methods will be introduced and implemented in R with applications to real data:

  • Principal Component Analysis (PCA)
  • K-means clustering
  • Hierarchical clustering
  • Spectral clustering
  • Google PageRank Algorithm
Date Time
Tuesday, 20.06.2023 09:00 - 17:00
Wednesday, 21.06.2023 09:00 - 17:00
Thursday, 22.06.2023 09:00 - 17:00
Grundlegende Kenntnisse in R

Überblick zu modernen Verfahren des maschinellen Lernens

Grenzen des maschinellen Lernens und der künstlichen Intelligenz

The following book is a good starting point: T. Hastie, R. Tibshirani, J. Friedman: The elements of statistical learning. Springer, 2009.
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