DSS
Decision Sciences & Systems
Technical University of Munich
 

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

Lectures + Tutorials in WS 17/18

Organisation

  • Time and place:
    Lecture: Thursday, 08:30-10:00,  102, Interims Hörsaal 2 (5620.01.102)
  • 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

Description

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 Data Mining Cup (DMC). This is a graded 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 25% and the exam by 75%. 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

 

Syllabus

19.10. Introduction (overview, recap of inferential statistics)

26.10. Regression Analysis (estimators, test theory, OLS)

02.11. Regression Diagnostics (Gauss-Markov theorem, GM assumptions, omitted variable bias, panel data analysis)

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

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

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

30.11.. Data Preparation and Causal Inference (practical data preparation, causal inference, IV, PSM, multiple imputation, etc.)

07.12 Model Selection and Learning Theory (model selection, gain curves, lift, ROC, bias-variance tradeoff, Kolmogorov complexity, MDL)

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

21.12. Introduction to the Data Mining Cup (R tutorial on data preparation and evaluation)

11.01. Guest Lecture 

18.01. High-Dimensional Problems (PCA, SVD, PCA regression, PLS, ridge regression, LASSO)

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

01.02. Presentation Data Mining Cup

Final Exam

Literature

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)
  • Jay Kearns: Introduction to Probability and Statistics using R, 2010 (E-Book)

Contacts:

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.

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