Decision Sciences & Systems
Technical University of Munich

Prof. Dr. Martin Bichler

Nils Kohring M.Sc. | Stefan Heidekrüger M.Sc. | Paul Sutterer M.Sc. | Johannes Knörr M.Sc. | Dr. Paul Karänke

Lectures & Tutorials in WS 20/21



  • News and materials will be uploaded to Moodle.
  • Late registrations are possible.

For any administrative issues, please contact Nils Kohring.



This is an introductory course in data analysis with a focus on various methods for causal inference and applications in business and economics. The analysis of human choice behavior is particularly challenging in this domain and different from other fields of data analysis and machine learning. The participants will learn wide-spread methods for numerical prediction, classification, clustering, and dimensionality reduction. During tutorials, students will compute examples by hand and analyze data with the R language. The participants will be able to apply their knowledge during the Analytics Cup. This is a graded optional project where they get to analyze realistic data sets. If the grade in this project is better than the exam grade, it will be weighted by 33% and the exam by 67%. Therefore, participating students can only improve their grades. 

Students from IN, GE, and DE&A can choose only one of the following classes:

Data Mining, IN2023, 2V, WS, Prof. Runkler
Business Analytics, IN2028, 2V+2Ü, WS, Prof. Bichler
Data Analysis and Visualization in R, IN2339, 2V+4Ü, SS, Prof. Gagneur



05.11. Regression Analysis (estimators, test theory, OLS)
12.11. Regression Diagnostics (Gauss-Markov theorem, GM assumptions, omitted variable bias, panel data analysis)
19.11. Logistic and Poisson Regression (GLMs, logit, probit, poisson regression)
26.11. Naïve Bayes and Bayes Nets (Bayes rule, learning Bayes nets, d-separation)
03.12. Decision Tree Classifiers (entropy, C4.5, CART, tree pruning)
10.12. Data Preparation and Causal Inference (practical data preparation, causal inference, IV, PSM, multiple imputation, etc.)
17.12 Model Selection (gain curves, lift, ROC, bias-variance tradeoff) and Introduction to the Analytics Cup (R tutorial)
01.01. Ensemble Methods and Clustering (bagging, random forests, boosting, hierarchical clustering, k-means, expectation maximization)
16.01. High-Dimensional Problems (PCA, SVD, PCA regression, PLS, ridge regression, LASSO)
21.01. Association Rules and Recommenders (APRIORI, collaborative filtering: SVD-based and nearest neighbor), Neural Networks Intro
28.01. Neural Networks (feed-forward networks, backpropagation, gradient descent)
04.02. Convex Optimization (subgradient methods, online convex optimization)
11.02. Presentation Analytics Cup



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, 2016. (E-Book)
  • Ian Witten, Eibe Frank, Mark Hall, Christopher Pal: Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kauffman, 2016 (E-Book)
  • James H. Stock and Mark W. Watson: Introduction to Econometrics, Pearson Education.
  • Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani: An Introduction to Statistical Learning, Springer, 2014 (E-Book)
  • Hadley Wickham, Garrett Grolemund: R for Data Science, 2017 (E-Book)  



Nils Kohring, M.Sc.
Room 01.10.055
Phone: 289-17506
E-Mail: nils(.)kohring(at)in(.)tum(.)de

Stefan Heidekrüger, M.Sc. 
Room 01.10.056
E-Mail: stefan(.)heidekrueger(at)in(.)tum(.)de

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.

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


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