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 tutorial session
• The final grade of praktikum depends on the marks in each submitted assigment

Start: We start off on Monday, 28.04.2014, with the general introduction to tutorial and implementation tools.

Registration:

• You can register for Praktikum through LSF
• For further details please contact Umer Khan

Preliminaries: This course is especially suitable for students who attended one of the following lectures last term: Business Analytics, Machine Learning, Artificial Intelligence