Master Seminar Kognitive Systeme (WS 2012/2013)

Topic: Advanced Machine Learning (using RapidMiner)

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

Data acquirement and pre-processing,
Feature engineering,
Model evaluation, and
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.
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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