Decision Sciences & Systems
Technical University of Munich

Prof. Dr. Martin Bichler, Paul Sutterer M.Sc., Stefan Heidekrüger M.Sc., Dr. Paul Karänke

Lectures + Tutorials in WS 19/20


  • Time and place:
    Lecture: Thursday, 08:00-10:00,  00.02.001, MI HS 1, Friedrich L. Bauer Hörsaal
  • Description: 
    Module description IN2028.
  • Requirements: 
    Introductory classes on statistics and algorithms 
  • Registration:
    To participate please register via TUMonline
  • 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 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 prediciton, 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



17.10. Introduction (overview, recap of inferential statistics)
24.10. Regression Analysis (estimators, test theory, OLS)
31.10. Regression Diagnostics (Gauss-Markov theorem, GM assumptions, omitted variable bias, panel data analysis)
07.11. Logistic and Poisson Regression (GLMs, logit, probit, poisson regression)
14.11. Naïve Bayes and Bayes Nets (Bayes rule, learning Bayes nets, d-separation)
21.11. Decision Tree Classifiers (entrophy, C4.5, CART, tree pruning)
28.11. Data Preparation and Causal Inference (practical data preparation, causal inference, IV, PSM, multiple imputation, etc.)
05.12. Model Selection and Learning Theory (model selection, gain curves, lift, ROC, bias-variance tradeoff, Kolmogorov complexity, MDL)
12.12. Ensemble Methods and Clustering (bagging, random forests, boosting, hierarchical clustering, k-means, expectation maximization)
19.12. Guest Lecture at H4 Hotel Messestadt. Please register on moodle!
09.01. Introduction to the Analytics Cup (R tutorial on data preparation and evaluation)
16.01. High-Dimensional Problems (PCA, SVD, PCA regression, PLS, ridge regression, LASSO)
23.01. Association Rules and Recommenders (apriori, collaborative filtering: SVD-based and nearest neighbour)
30.01. Neural Networks (backpropagation, feed forward networks, perceptron)
06.02. 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)  



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.

Nils Kohring, M.Sc.
Room 01.10.055
Phone: 289-17506
E-Mail: nils.kohringzzin.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.

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