Decision Sciences & Systems
Technical University of Munich

Prof. Dr. Martin Bichler, Paul Sutterer M.Sc., Dr. Paul Karänke, Dr. Vladimir Fux

Lectures + Tutorials in WS 16/17

Business Analytics (IN2028)


  • Time and place:
    Lecture: Monday, 3.30-5.00 pm, Gustav-Niemann Hörsaal 2 (5510.EG.001)
  • Description: 
    Module description.
  • Requirements:
  • Fundamental of algorithms and datastructures and statistics 
  • Registration:
    To participate please register via TUMonline. Registration is open.
  • Tutorials:
    Seminar room MI 01.10.011
    Further information regarding tutorials is here
  • Exam:
    At the end of the course the 90-minute exam will take place.
    Exam details can be found here


This is an introductory course in data analysis with a focus on various models to understand and predict human (choice) behavior from large data sets. The participants will familiarize themselves with relevant methods from econometrics and data mining as they are widely used in marketing. They will learn various methods for classification, numerical prediction, and clustering, which focus on wide-spread problems in business practice. During tutorials, the instructors will show how to analyze data with the R language. The participants will be able to apply their knowledge during the Data Mining Cup (DMC), an exercise where they get to analyze realistic data sets. 

Relevant articles:


24.10. Introduction and Statistics

31.10. Inferential Statistics (estimators, test theory, OLS)

07.11. Multi-linear Regression and Panel Data (Gauss-Markov theorem, regression analysis, panel data analysis, omitted variable bias)

14.11. Logistic and Poisson Regression (GLMs, logit, probit, poisson regression)

21.11. Naïve Bayes and Bayes Nets (Bayes rule, learning Bayes nets, inference, d-separation)

28.11. Decision Tree Classifiers (entrophy, C4.5, CART, tree pruning)

05.12. Data Preparation (model selection, causal inference and selection bias, PSM, multiple imputation, etc.)

12.12. Evaluation of Classifiers and Learning Theory (gain curves, lift, ROC, bias-variance tradeoff, Kolmogorov complexity, MDL)

19.12. Introduction into the Data Mining Cup (R tutorial on data preparation and evaluation)

09.01. Guest Lecture (Mr. Heimann - Telefónica)

16.01. Ensemble Methods and Clustering (bagging, random forests, boosting, hierarchical clustering, k-means, expectation maximization)

23.01. Dimensionality Reduction (PCA, SVD, PCA regression, PLS, ridge regression, LASSO)

30.01. Association Rules and Recommenders (apriori, collaborative filtering: SVD-based and nearest neighbour)

06.02. Presentation Data Mining Cup

Final Exam


The presentation slides for the lectures and tutorials are accessible via MOODLE. The contents of the lectures can be found in chapters from the following textbooks:

  • Trevor Hastie, Jerome Friedman, Robert Tibshirani: Elements of Statistical Learning, Springer, 2014. (E-Book)
  • Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani: An Introduction to Statistical Learning, Springer, 2014 (E-Book)
  • Ian Witten, Eibe Frank: Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kauffman, 2011 (E-Book)
  • Jay Kearns: Introduction to Probability and Statistics using R, 2010 (E-Book)



Prof. Dr. Martin Bichler 
Room 01.10.061 (Garching) 
Phone: 289-17534 
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it. 
Sprechstunde nach Vereinbarung

Paul Sutterer, M.Sc. 
Room 01.10.055
Phone: 289-17507
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Dr. Paul Karänke
Room 01.10.057
Phone: 289-17504
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Dr. Vladimir Fux
Room 01.10.058
Phone: 289-17528
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

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