Master Seminar Kognitive Systeme (WS 2011/2012)

Topic: Practical Aspects of Machine Learning (using RapidMiner)

In practice machine learning is more than algorithms classifying examples. There are many things around it:

1. Data acquirement and pre-processing,
2. Feature engineering,
3. Model evaluation, and
4. Software that performs these tasks.

In this seminary we will emphasize the practical aspects of these surroundings. Our experience will be based on RapidMiner – an open-source data mining tool. First we will learn how to use this software:

• Process design concept
• Importing, exporting, and generating data
• Basic mechanisms: loops, macros, logging, ...

Then we will center on theoretic concepts (e.g., feature generation, evaluation techniques) along with examples (PCA, bootstrap validation) and their realizations in RapidMiner. Finally we will take a look at competitive products and how to extend RapidMiner.

Possible PresentationTopics

• Feature Generation
• Disretization
• Feature Selection
• Extending RapidMiner
• Competitive products

Literature (available in the vc course)

• Witten, I. H. & Eibe, F. (2005). Data Mining. Practical Machine Learning Tools and Techniques. Elsevier. (Chapter 1)
• Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques Elsevier. (Chapter 1)

Relevant literature for the single topics will be provided within sessions.

General Information

• You find a general course description at the corresponding pages from the WIAI module guide.
• You find administrative information at UnivIS.
• Participants should sign up for the course in the virtual campus.
• This course is open for master and advanced bachelor students.
• Prerequisites: Basic machine learning knowledge as taught in our Machine Learning course (especially the first three lectures) will be helpful.
• Presentations and theses may be given/written in German or English.

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