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Courses in winter term 2017 / Praktikum Machine Learning & Artificial Intelligence

This implementation-oriented course offers hands-on experience with current algorithms and approaches in Machine Learning and Artificial Intelligence, and their application to real-world learning and decision-making tasks. Praktikum will also cover empirical methods for comparing learning algorithms, for understanding and explaining their differences, for analyzing the conditions in which a method is more suitable than others.

On weekly or bi-weekly basis, we shall implement linear models for predictions (Linear Regression, Logistic Regression), classification trees (Decision trees), prototype method for clustering (K-Means), prototype classification methods (K-Nearest Neighbor, Naive Bayes classifier, Support Vector Machines) and link-based ranking algorithm PageRank.

  • Tutorials will be held every week
  • An implementation assignment will be given every week
  • A solution to the assignments is discussed in the next lab session
  • The final grade of praktikum depends on the points in each submitted assigment

Start: We start off on Monday/ Thursday, 23./26.10.2016, with the general introduction to tutorial and implementation tools.


  1. Kevin P. Murphy (2012): Machine Learning, A Probabilistic Approach, MIT Press.
  2. Wes McKinney (2012): Python for Data Analysis Data Wrangling with Pandas, NumPy, and IPython, O’Reilly
  3. Joel Grus (2015): Data Science from Scratch First Principles with Python, O’Reilly
  4. Willi Richert, Luis Pedro Coelho (2013): Building Machine Learning Systems with Python, PACKT


  • You can register for Praktikum through LSF

Time: Mo 14-18 or Thu 10-14
Location: C 147
Begin: 23.10.2017 or 26.10.2017
Assignment: Data Analytics & MSc WI & IMIT
Modul- Handbuch:MHB
Last Lecture: here