General Information on the Lecture, Term SS 11:

EDV Nr.	21308
Date:	Di 14.15h-17.30h
Room:	136
SWS/ECTS:	4/4

Announcements:

Content

In this course 6 different data mining and pattern recognition applications are implemented by all student groups. A group contains at most 3 students. The implementation of each application should be done within one afternoon (14.15h-17.30h). The applications are:

Data Mining Process: Steps of the entire Data Mining Process in general are demonstrated using the Orange Data Mining Tool.

Mining Data from Amazon.com: Using the Amazon Web Service (AWS) one can access loads of product and review data from Amazon.com. In this exercise we integrate the data in our programms using a python wrapper for AWS. Then we apply various intelligent algorithms for mining interesting knowledge out of this data. E.g. we perform trend analysis or predict price models. Students are free to develop their own data mining applications

Recommender Systems: Recommender Systems are applied in E-commerce for generating customized recommendations. Well known are the Amazon.com recommendations which are either distributed by e-mail or presented on the Amazon web page after login. For generating these recommendations the products which have already purchased or reviewed by the user are taken into account. In this exercise the currently most popular algorithms (Collaborative Filtering) for generating recommendations are implemented, tested and analysed.

Spam Filter: A Naive Bayes Classifier is implemented for filtering spam. It is also shown how to apply this algorithm for document classification in general

Document Classification and Feature Extraction: In this excercise a large amount of RSS-Newsfeeds is collected. All articles coming from the different feeds are clustered using non-negative matrix factorisation. Essential features of each document cluster are extracted

Face Recognition: In this excercise a programm for face recognition is implemented. For a given set of training images (biometrical face photos) the Principal Component Analysis (PCA) is applied to calculate the space of eigenfaces. Then a photo which has to be recognized is transformed to the space of eigenfaces and the closest training photo is calculated.


All applications are implemented in Python.
In addition, students have to prepare presentations. Goal of the presentations is a sufficient introduction for the lab exercises. The presentations are therefore scheduled before the first lab exercise. Each presentation should last 90 minutes.

Vortrag	Python	Machine Learning	Orange	Recommender	Doc Classification	Face Recognition
Gruppe	Maucher	Gruppe 1	Gruppe 2	Gruppe 3	Gruppe 4	Gruppe 5
22.03.11	Presentation	Audience	Audience	Audience	Audience	Audience
05.04.11	Audience	Presentation	Presentation	Audience	Audience	Audience
12.04.11	Audience	Audience	Audience	Presentation	Presentation	Audience
19.04.11	Data Mining Process and Orange
26.04.11	Mining Data from Amazon.com
10.05.11	Recommender Systems
17.05.11	Document Classification / Spam Filtering
24.05.11	Feature Extraction & Document Clustering (Newsfeeds)
31.05.11	Face Recognition Presentation (90 min) and start of Face Recog. Exercise
07.06.11	Face Recognition Exercise